ORCID Profile
0000-0003-1769-6430
Current Organisation
Macquarie University
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Applied Statistics | Large and complex data theory | Applied statistics | Demography | Migration | Statistics | Statistical theory |
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 09-2011
Publisher: Elsevier BV
Date: 02-2021
Publisher: Cambridge University Press (CUP)
Date: 18-02-2020
DOI: 10.1017/ASB.2020.3
Abstract: When modelling subnational mortality rates, we should consider three features: (1) how to incorporate any possible correlation among subpopulations to potentially improve forecast accuracy through multi-population joint modelling (2) how to reconcile subnational mortality forecasts so that they aggregate adequately across various levels of a group structure (3) among the forecast reconciliation methods, how to combine their forecasts to achieve improved forecast accuracy. To address these issues, we introduce an extension of grouped univariate functional time-series method. We first consider a multivariate functional time-series method to jointly forecast multiple related series. We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. Using the Japanese regional age-specific mortality rates, we investigate 1–15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations.
Publisher: Informa UK Limited
Date: 10-02-2021
Publisher: Informa UK Limited
Date: 03-03-2020
Publisher: Springer Science and Business Media LLC
Date: 04-12-2014
Publisher: Informa UK Limited
Date: 14-05-2014
Publisher: Bernoulli Society for Mathematical Statistics and Probability
Date: 05-2023
DOI: 10.3150/22-BEJ1508
Publisher: Elsevier BV
Date: 07-2021
Publisher: Springer Science and Business Media LLC
Date: 09-11-2020
Publisher: Informa UK Limited
Date: 02-09-2015
DOI: 10.1080/00324728.2015.1074268
Abstract: Although there are continuing developments in the methods for forecasting mortality, there are few comparisons of the accuracy of the forecasts. The subject of the statistical validity of these comparisons, which is essential to demographic forecasting, has all but been ignored. We introduce Friedman's test statistics to examine whether the differences in point and interval forecast accuracies are statistically significant between methods. We introduce the Nemenyi test statistic to identify which methods give results that are statistically significantly different from others. Using sex-specific and age-specific data from 20 countries, we apply these two test statistics to examine the forecast accuracy obtained from several principal component methods, which can be categorized into coherent and non-coherent forecasting methods.
Publisher: Elsevier BV
Date: 05-2022
Publisher: Wiley
Date: 12-10-2022
DOI: 10.1002/CEM.3452
Abstract: Scalar‐on‐function regression, where the response is scalar valued and the predictor consists of random functions, is one of the most important tools for exploring the functional relationship between a scalar response and functional predictor(s). The functional partial least‐squares method improves estimation accuracy for estimating the regression coefficient function compared to other existing methods, such as least squares, maximum likelihood, and maximum penalized likelihood. The functional partial least‐squares method is often based on the SIMPLS or NIPALS algorithm, but these algorithms can be computationally slow for analyzing a large dataset. In this study, we propose two modified functional partial least‐squares methods to efficiently estimate the regression coefficient function under the scalar‐on‐function regression. In the proposed methods, the infinite‐dimensional functional predictors are first projected onto a finite‐dimensional space using a basis expansion method. Then, two partial least‐squares algorithms, based on re‐orthogonalization of the score and loading vectors, are used to estimate the linear relationship between scalar response and the basis coefficients of the functional predictors. The finite‐s le performance and computing speed are evaluated using a series of Monte Carlo simulation studies and a sugar process dataset.
Publisher: Wiley
Date: 2023
DOI: 10.1002/STA4.621
Publisher: Cambridge University Press (CUP)
Date: 17-09-2019
DOI: 10.1017/S1748499519000101
Abstract: We consider a compositional data analysis approach to forecasting the age distribution of death counts. Using the age-specific period life-table death counts in Australia obtained from the Human Mortality Database, the compositional data analysis approach produces more accurate 1- to 20-step-ahead point and interval forecasts than Lee–Carter method, Hyndman–Ullah method and two naïve random walk methods. The improved forecast accuracy of period life-table death counts is of great interest to demographers for estimating survival probabilities and life expectancy, and to actuaries for determining temporary annuity prices for various ages and maturities. Although we focus on temporary annuity prices, we consider long-term contracts that make the annuity almost lifetime, in particular when the age at entry is sufficiently high.
Publisher: Elsevier BV
Date: 09-2022
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 07-01-2020
Publisher: Springer International Publishing
Date: 2017
Publisher: Wiley
Date: 07-03-2019
DOI: 10.1111/EUFM.12212
Publisher: Springer Science and Business Media LLC
Date: 07-12-2018
Publisher: Elsevier BV
Date: 03-2011
Publisher: Institute of Mathematical Statistics
Date: 06-2022
DOI: 10.1214/21-BJPS523
Publisher: Springer Science and Business Media LLC
Date: 02-04-2013
Publisher: Max Planck Institute for Demographic Research
Date: 15-07-2011
Publisher: Wiley
Date: 23-04-2014
DOI: 10.1002/PSP.1856
Publisher: Informa UK Limited
Date: 16-09-2019
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 23-04-2022
Publisher: Informa UK Limited
Date: 05-11-2012
Publisher: Wiley
Date: 12-2022
DOI: 10.1111/ANZS.12376
Abstract: Permutation entropy (PE) is an ordinal‐based non‐parametric complexity measure for studying the temporal dependence structure in a linear or non‐linear time series. Based on the PE, we propose a new measure, namely permutation dependence (PD), to quantify the strength of the temporal dependence in a univariate time series and remedy the major drawbacks of PE. We demonstrate that the PE and PD are viable and useful alternatives to conventional temporal dependence measures, such as the autocorrelation function (ACF) and mutual information (MI). Compared to the ACF, the PE and PD are not restricted in detecting the linear or quasi‐linear serial correlation in an autoregression model. Instead, they can be viewed as non‐parametric and non‐linear alternatives since they do not require any prior knowledge or assumptions about the underlying structure. Compared to MI estimated by k ‐nearest neighbour, PE and PD show added sensitivity to structures of relatively weak strength. We compare the finite‐s le performance of the PE and PD with the ACF and the MI estimated by k ‐nearest neighbour in a number of simulation studies to showcase their respective strengths and weaknesses. Moreover, their performance under non‐stationarity is also investigated. Using high‐frequency EUR/USD exchange rate returns data, we apply the PE and PD to study the temporal dependence structure in intraday foreign exchange.
Publisher: Informa UK Limited
Date: 06-01-2021
Publisher: Elsevier BV
Date: 07-2022
Publisher: Wiley
Date: 07-04-2017
DOI: 10.1002/FOR.2467
Publisher: Springer Science and Business Media LLC
Date: 07-11-2013
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 29-11-2015
Publisher: Springer Science and Business Media LLC
Date: 10-2022
DOI: 10.1007/S10182-022-00463-7
Abstract: We consider a sieve bootstrap procedure to quantify the estimation uncertainty of long-memory parameters in stationary functional time series. We use a semiparametric local Whittle estimator to estimate the long-memory parameter. In the local Whittle estimator, discrete Fourier transform and periodogram are constructed from the first set of principal component scores via a functional principal component analysis. The sieve bootstrap procedure uses a general vector autoregressive representation of the estimated principal component scores. It generates bootstrap replicates that adequately mimic the dependence structure of the underlying stationary process. We first compute the estimated first set of principal component scores for each bootstrap replicate and then apply the semiparametric local Whittle estimator to estimate the memory parameter. By taking quantiles of the estimated memory parameters from these bootstrap replicates, we can nonparametrically construct confidence intervals of the long-memory parameter. As measured by coverage probability differences between the empirical and nominal coverage probabilities at three levels of significance, we demonstrate the advantage of using the sieve bootstrap compared to the asymptotic confidence intervals based on normality.
Publisher: Elsevier BV
Date: 03-2019
Publisher: Oxford University Press (OUP)
Date: 07-06-2022
DOI: 10.1111/RSSC.12572
Abstract: We consider determining the optimal stopping time for the glue curing of wood panels in an automatic process environment. Using the near-infrared spectroscopy technology to monitor the manufacturing process ensures substantial savings in energy and time. We collect a time-series of curves from a near-infrared spectrum probe consisting of 72 spectra and aim to detect an optimal stopping time. We propose an estimation procedure to determine the optimal stopping time of wood panel compression and the estimation uncertainty associated with the estimated stopping time. Our method first ides the entire data set into a training s le and a testing s le, then iteratively computes integrated squared forecast errors based on the testing s le. We then apply a structural break detection method with one breakpoint to determine an estimated optimal stopping time from a univariate time-series of the integrated squared forecast errors. We also investigate the finite s le performance of the proposed method via a series of simulation studies.
Publisher: Springer Science and Business Media LLC
Date: 08-09-2020
DOI: 10.1186/S41118-020-00099-Y
Abstract: Accuracy in fertility forecasting has proved challenging and warrants renewed attention. One way to improve accuracy is to combine the strengths of a set of existing models through model averaging. The model-averaged forecast is derived using empirical model weights that optimise forecast accuracy at each forecast horizon based on historical data. We apply model averaging to fertility forecasting for the first time, using data for 17 countries and six models. Four model-averaging methods are compared: frequentist, Bayesian, model confidence set, and equal weights. We compute in idual-model and model-averaged point and interval forecasts at horizons of one to 20 years. We demonstrate gains in average accuracy of 4–23% for point forecasts and 3–24% for interval forecasts, with greater gains from the frequentist and equal weights approaches at longer horizons. Data for England and Wales are used to illustrate model averaging in forecasting age-specific fertility to 2036. The advantages and further potential of model averaging for fertility forecasting are discussed. As the accuracy of model-averaged forecasts depends on the accuracy of the in idual models, there is ongoing need to develop better models of fertility for use in forecasting and model averaging. We conclude that model averaging holds considerable promise for the improvement of fertility forecasting in a systematic way using existing models and warrants further investigation.
Publisher: Springer Science and Business Media LLC
Date: 09-06-2012
Publisher: Cambridge University Press (CUP)
Date: 20-06-2019
DOI: 10.1017/ASB.2019.20
Abstract: In areas of application, including actuarial science and demography, it is increasingly common to consider a time series of curves an ex le of this is age-specific mortality rates observed over a period of years. Given that age can be treated as a discrete or continuous variable, a dimension reduction technique, such as principal component analysis (PCA), is often implemented. However, in the presence of moderate-to-strong temporal dependence, static PCA commonly used for analyzing independent and identically distributed data may not be adequate. As an alternative, we consider a dynamic principal component approach to model temporal dependence in a time series of curves. Inspired by Brillinger’s (1974, Time Series: Data Analysis and Theory. New York: Holt, Rinehart and Winston) theory of dynamic principal components, we introduce a dynamic PCA, which is based on eigen decomposition of estimated long-run covariance. Through a series of empirical applications, we demonstrate the potential improvement of 1-year-ahead point and interval forecast accuracies that the dynamic principal component regression entails when compared with the static counterpart.
Publisher: Elsevier BV
Date: 07-2016
Publisher: Informa UK Limited
Date: 06-04-2020
Publisher: Springer Science and Business Media LLC
Date: 09-2009
Publisher: Springer Science and Business Media LLC
Date: 09-2009
Publisher: Physica-Verlag HD
Date: 2008
Publisher: Informa UK Limited
Date: 19-04-2023
Publisher: Elsevier BV
Date: 07-2022
Publisher: Informa UK Limited
Date: 25-01-2020
Publisher: Springer Science and Business Media LLC
Date: 06-03-2021
Publisher: Informa UK Limited
Date: 25-08-2014
Publisher: Informa UK Limited
Date: 31-01-2019
Publisher: Wiley
Date: 23-02-2016
DOI: 10.1111/INSR.12163
Publisher: Vilnius Gediminas Technical University
Date: 27-04-2022
Abstract: In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space via the functional principal component analysis paradigm in the estimation phase. It is then approximated using the estimated functional principal component functions, and the estimated parameter of the quantile regression model is constructed based on the principal component scores. In addition, we propose a Bayesian information criterion to determine the optimum number of truncation constants used in the functional principal component decomposition. Moreover, a stepwise forward procedure and the Bayesian information criterion are used to determine the significant predictors for including in the model. We employ a nonparametric bootstrap procedure to construct prediction intervals for the response functions. The finite s le performance of the proposed method is evaluated via several Monte Carlo experiments and an empirical data ex le, and the results produced by the proposed method are compared with the ones from existing models.
Publisher: Informa UK Limited
Date: 30-05-2019
Publisher: Springer India
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 21-11-2018
Publisher: Elsevier BV
Date: 11-2017
Publisher: MDPI AG
Date: 07-2020
DOI: 10.3390/RISKS8030069
Abstract: An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can take place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time-series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past.
Publisher: Wiley
Date: 17-01-2022
DOI: 10.1002/FOR.2849
Abstract: Using the ordinal‐pattern concept in permutation entropy, we propose a model sufficiency test to study a given model's point prediction accuracy. Compared with some classical model sufficiency tests, such as Broock et al.'s (1996) test, our proposal does not require a sufficient model to eliminate all structures exhibited in the estimated residuals. When the innovations in the investigated data's underlying dynamics show a certain structure, such as higher moment serial dependence, Broock et al.'s (1996) test can lead to erroneous conclusions about the sufficiency of point predictors. Due to the structured innovations, inconsistency between the model sufficiency tests and prediction accuracy criteria can occur. Our proposal fills in this incoherence between model and prediction evaluation approaches and remains valid when the underlying process has nonwhite additive innovation.
Publisher: Walter de Gruyter GmbH
Date: 02-01-2020
Abstract: The Hurst exponent is the simplest numerical summary of self-similar long-range dependent stochastic processes. We consider the estimation of Hurst exponent in long-range dependent curve time series. Our estimation method begins by constructing an estimate of the long-run covariance function, which we use, via dynamic functional principal component analysis, in estimating the orthonormal functions spanning the dominant sub-space of functional time series. Within the context of functional autoregressive fractionally integrated moving average (ARFIMA) models, we compare finite-s le bias, variance and mean square error among some time- and frequency-domain Hurst exponent estimators and make our recommendations.
Publisher: Informa UK Limited
Date: 28-12-2018
Publisher: Elsevier BV
Date: 10-2019
Publisher: Informa UK Limited
Date: 2010
Publisher: Elsevier BV
Date: 10-2014
Publisher: Wiley
Date: 14-08-2021
DOI: 10.1002/CJS.11652
Abstract: We study a functional version of fractionally integrated time series that covers the nonstationary case when the memory parameter d is above 0.5. We project time series, with varying levels of nonstationarity, onto a finite‐dimensional subspace. We obtain the eigenvalues and eigenfunctions that span a s le version of the dominant subspace through dynamic functional principal component analysis of the s le long‐run covariance functions. Within the context of functional autoregressive fractionally integrated moving average models, we evaluate and compare finite‐s le bias and mean‐squared error among some time‐ and frequency‐domain Hurst exponent estimators via Monte Carlo simulations. We apply the estimators to Canadian female and male life‐table death counts.
Publisher: Oxford University Press (OUP)
Date: 28-02-2018
DOI: 10.1111/RSSA.12359
Abstract: Visualization methods help in the discovery of characteristics that might not have been apparent by using mathematical models and summary statistics. However, visualization methods have not received much attention in demography, with the exceptions of scatter plots and Lexis surfaces. We utilize a phase plane plot to visualize the rate of change, obtained from derivatives of a continuous function. The phase plane plot bears a resemblance to hysteresis loops, isogrowth curves and solutions to differential equations. Using Australian and Chilean fertility, we present phase plane plots. Similarly to the scatter plot and Lexis surface, the phase plane plot identifies the age with maximum fertility rate and displays skewness of the fertility distribution. Unlike the scatter plot and Lexis surface, the phase plane plot identifies the age with maximum positive or negative velocity (i.e. the trend) and can compare the magnitude of the rate of change between any two years on the basis of the size of the radius of circles. The phase plane plot enables visualization of dynamic changes in fertility for a given age over the years and is potentially useful for visualizing dynamic changes in birth cohort fertility. Via the animate package in LATEX, a dynamic phase plane plot is also proposed to visualize changes in fertility over age or year.
Publisher: Elsevier BV
Date: 2017
Publisher: Informa UK Limited
Date: 12-2020
Publisher: Wiley
Date: 21-03-2022
DOI: 10.1002/CEM.3394
Abstract: The scalar‐on‐function regression model has become a popular analysis tool to explore the relationship between a scalar response and multiple functional predictors. Most of the existing approaches to estimate this model are based on the least‐squares estimator, which can be seriously affected by outliers in empirical datasets. When outliers are present in the data, it is known that the least‐squares‐based estimates may not be reliable. This paper proposes a robust functional partial least squares method, allowing a robust estimate of the regression coefficients in a scalar‐on‐multiple‐function regression model. In our method, the functional partial least squares components are computed via the partial robust M‐regression. The predictive performance of the proposed method is evaluated using several Monte Carlo experiments and two chemometric datasets: glucose concentration spectrometric data and sugar process data. The results produced by the proposed method are compared favorably with some of the classical functional or multivariate partial least squares and functional principal component analysis methods.
Publisher: Informa UK Limited
Date: 18-04-2022
Publisher: Wiley
Date: 04-11-2020
DOI: 10.1002/FOR.2732
Abstract: We study causality between bivariate curve time series using the Granger causality generalized measures of correlation. With this measure, we can investigate which curve time series Granger‐causes the other in turn, it helps determine the predictability of any two curve time series. Illustrated by a climatology ex le, we find that the sea surface temperature Granger‐causes sea‐level atmospheric pressure. Motivated by a portfolio management application in finance, we single out those stocks that lead or lag behind Dow Jones industrial averages. Given a close relationship between S& P 500 index and crude oil price, we determine the leading and lagging variables.
Publisher: Springer Science and Business Media LLC
Date: 08-09-2016
Publisher: Springer Science and Business Media LLC
Date: 27-09-2021
Publisher: Wiley
Date: 19-12-2016
DOI: 10.1111/JTSA.12229
Publisher: Elsevier BV
Date: 05-2021
Publisher: Institute of Mathematical Statistics
Date: 09-2016
DOI: 10.1214/16-AOAS953
Publisher: Wiley
Date: 06-2021
DOI: 10.1111/ANZS.12330
Abstract: We forecast the old‐age dependency ratio for Australia under various pension age proposals, and estimate a pension age scheme that will provide a stable old‐age dependency ratio at a specified level. Our approach involves a stochastic population forecasting method based on coherent functional data models for mortality, fertility and net migration, which we use to simulate the future age‐structure of the population. Our results suggest that the Australian pension age should be increased to 68 by 2030, 69 by 2036 and 70 by 2050, in order to maintain the old‐age dependency ratio at 23%, just above the 2018 level. Our general approach can easily be extended to other target levels of the old‐age dependency ratio and to other countries.
Publisher: Elsevier BV
Date: 08-2022
Publisher: MDPI AG
Date: 25-03-2017
DOI: 10.3390/RISKS5020021
Publisher: School of Statistics, Renmin University of China
Date: 2022
DOI: 10.6339/21-JDS1031
Abstract: We study the importance of group structure in grouped functional time series. Due to the non-uniqueness of group structure, we investigate different disaggregation structures in grouped functional time series. We address a practical question on whether or not the group structure can affect forecast accuracy. Using a dynamic multivariate functional time series method, we consider joint modeling and forecasting multiple series. Illustrated by Japanese sub-national age-specific mortality rates from 1975 to 2016, we investigate one- to 15-step-ahead point and interval forecast accuracies for the two group structures.
Publisher: Elsevier BV
Date: 07-2017
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C4CP03711A
Abstract: An atomistic study of Ge-core–Si-shell nanocrystals gives a detailed picture of how strain and confinement effect the electronic and optical properties.
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 04-2016
Publisher: Informa UK Limited
Date: 16-09-2021
Publisher: Max Planck Institute for Demographic Research
Date: 09-11-2012
Publisher: Institute of Mathematical Statistics
Date: 12-2022
DOI: 10.1214/22-AOAS1602
Publisher: MDPI AG
Date: 22-04-2016
Publisher: MDPI AG
Date: 14-06-2022
Abstract: This study quantifies the air quality impact on population mortality from an actuarial perspective, considering implications to the industry through the application of findings. The study focuses on the increase in mortality from air quality changes due to extreme weather impacts. We conduct an empirical study using monthly Californian climate and mortality data from 1999 to 2019 to determine whether adding PM2.5 as a factor improves forecast excess mortality. Expected mortality is defined using the rolling five-year average of observed mortality for each county. We compared three statistical models, namely a Generalised Linear Model (GLM), a Generalised Additive Model (GAM), and an Extreme Gradient Boosting (XGB) regression model. We find including PM2.5 improves the performance of all three models and that the GAM performs the best in terms of predictive accuracy. Change points are also considered to determine whether significant events trigger changes in mortality over extended periods. Based on several identified change points, some wildfires trigger heightened excess mortality.
Publisher: Informa UK Limited
Date: 27-02-2019
Publisher: MDPI AG
Date: 18-03-2022
Abstract: We propose a functional time series method to obtain accurate multi-step-ahead forecasts for age-specific mortality rates. The dynamic functional principal component analysis method is used to decompose the mortality curves into dynamic functional principal components and their associated principal component scores. Machine-learning-based multi-step-ahead forecasting strategies, which automatically learn the underlying structure of the data, are used to obtain the future realization of the principal component scores. The forecasted mortality curves are obtained by combining the dynamic functional principal components and forecasted principal component scores. The point and interval forecast accuracy of the proposed method is evaluated using six age-specific mortality datasets and compared favorably with four existing functional time series methods.
Publisher: Informa UK Limited
Date: 03-04-2017
Publisher: Wiley
Date: 14-06-2023
DOI: 10.1002/FOR.3000
Abstract: Intraday financial data often take the form of a collection of curves that can be observed sequentially over time, such as intraday stock price curves. These curves can be viewed as a time series of functions observed on equally spaced and dense grids. Due to the curse of dimensionality, high‐dimensional data pose challenges from a statistical aspect however, it also provides opportunities to analyze a rich source of information so that the dynamic changes within short‐time intervals can be better understood. We consider a sieve bootstrap method to construct 1‐day‐ahead point and interval forecasts in a model‐free way. As we sequentially observe new data, we also implement two dynamic updating methods to update point and interval forecasts for achieving improved accuracy. The forecasting methods are validated through an empirical study of 5‐min cumulative intraday returns of the S& P/ASX All Ordinaries Index.
Publisher: Springer Science and Business Media LLC
Date: 26-09-2022
DOI: 10.1007/S00180-022-01282-9
Abstract: The problem of estimating missing fragments of curves from a functional s le has been widely considered in the literature. However, most reconstruction methods rely on estimating the covariance matrix or the components of its eigendecomposition, which may be difficult. In particular, the estimation accuracy might be affected by the complexity of the covariance function, the noise of the discrete observations, and the poor availability of complete discrete functional data. We introduce a non-parametric alternative based on depth measures for partially observed functional data. Our simulations point out that the benchmark methods perform better when the data come from one population, curves are smooth, and there is a large proportion of complete data. However, our approach is superior when considering more complex covariance structures, non-smooth curves, and when the proportion of complete functions is scarce. Moreover, even in the most severe case of having all the functions incomplete, our method provides good estimates meanwhile, the competitors are unable. The methodology is illustrated with two real data sets: the Spanish daily temperatures observed in different weather stations and the age-specific mortality by prefectures in Japan. They highlight the interpretability potential of the depth-based method.
Publisher: Informa UK Limited
Date: 08-2019
Publisher: Walter de Gruyter GmbH
Date: 30-11-2020
Abstract: This paper presents a Bayesian s ling approach to bandwidth estimation for the local linear estimator of the regression function in a nonparametric regression model. In the Bayesian s ling approach, the error density is approximated by a location-mixture density of Gaussian densities with means the in idual errors and variance a constant parameter. This mixture density has the form of a kernel density estimator of errors and is referred to as the kernel-form error density (c.f. Zhang, X., M. L. King, and H. L. Shang. 2014. “A S ling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34.). While (Zhang, X., M. L. King, and H. L. Shang. 2014. “A S ling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34) use the local constant (also known as the Nadaraya-Watson) estimator to estimate the regression function, we extend this to the local linear estimator, which produces more accurate estimation. The proposed investigation is motivated by the lack of data-driven methods for simultaneously choosing bandwidths in the local linear estimator of the regression function and kernel-form error density. Treating bandwidths as parameters, we derive an approximate (pseudo) likelihood and a posterior. A simulation study shows that the proposed bandwidth estimation outperforms the rule-of-thumb and cross-validation methods under the criterion of integrated squared errors. The proposed bandwidth estimation method is validated through a nonparametric regression model involving firm ownership concentration, and a model involving state-price density estimation.
Publisher: Oxford University Press (OUP)
Date: 29-05-2022
DOI: 10.1111/RSSA.12859
Abstract: Taylor's law is a widely observed empirical pattern that relates the variances to the means of population densities. We present four extensions of the classical Taylor's law (TL): (1) a cubic extension of the linear TL describes the mean–variance relationship of human mortality at subnational levels well (2) in a time series, long-run variance measures not only variance but also autocovariance, and it is a more suitable measure than variance alone to capture temporal/spatial correlation (3) an extension of the classical equally weighted spatial variance takes account of synchrony and proximity (4) robust linear regression estimators of TL parameters reduce vulnerability to outliers. Applying the proposed methods to age-specific Japanese subnational death rates from 1975 to 2018, we study temporal and spatial variations, compare different coefficient estimators, and interpret the implications. We apply a clustering algorithm to the estimated TL coefficients and find that cluster memberships are strongly related to prefectural gross domestic product. The time series of spatial TL coefficients has a decreasing trend that confirms the narrowing gap between rural and urban mortality in Japan.
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 08-10-2016
Publisher: BMJ
Date: 03-2020
DOI: 10.1136/BMJOPEN-2019-034908
Abstract: GoodSAM is a mobile phone app that integrates with UK ambulance services. During a 999 call, if a call handler diagnoses cardiac arrest, nearby volunteer first responders registered with the app are alerted. They can give cardiopulmonary resuscitation (CPR) and/or use a public access automated external defibrillator (AED). We aimed to identify means of increasing AED use by GoodSAM first responders. We conducted semistructured telephone interviews, using the Theoretical Domains Framework to identify and classify barriers to AED use. We analysed findings using the Capability, Opportunity, Motivation, Behaviour (COM-B) model and subsequently used the Behaviour Change Wheel to develop potential interventions to improve AED use. London, UK. GoodSAM first responders alerted in the previous 7 days about a cardiac arrest. We conducted 30 telephone interviews in two batches in July and October 2018. A public access AED was taken to scene once, one had already been attached on scene another time and three participants took their own AEDs when responding. Most first responders felt capable and motivated to use public access AEDs but were concerned about delaying CPR if they retrieved one and frustrated when arriving after the ambulance service. They perceived lack of opportunities due to unavailable and inaccessible AEDs, particularly out of hours. We subsequently developed 13 potential interventions to increase AED use for future testing. GoodSAM first responders used AEDs occasionally, despite a capability and motivation to do so. Those operating volunteer first responder systems should consider our proposed interventions to improve AED use. Of particular clinical importance are: highlighting AED location and providing route/time estimates to the patient via the nearest AED. This would help single responders make appropriate decisions about AED retrieval. As AED collection may extend time to reach the patient, where there is sufficient density of potential responders, systems could send one responder to initiate CPR and another to collect an AED.
Publisher: Wiley
Date: 17-01-2021
DOI: 10.1111/JTSA.12577
Abstract: This article studies stationary functional time series with long‐range dependence, and estimates the memory parameter involved. Semiparametric local Whittle estimation is used, where periodogram is constructed from the approximate first score, which is an inner product of the functional observation and estimated leading eigenfunction. The latter is obtained via classical functional principal component analysis. Under the restrictive condition of constancy of the memory parameter over the function support, and other conditions which include rather unprimitive ones on the first score, the estimate is shown to be consistent and asymptotically normal with asymptotic variance free of any unknown parameter, facilitating inference, as in the scalar time series case. Although the primary interest lies in long‐range dependence, our methods and theory are relevant to short‐range dependent or negative dependent functional time series. A Monte Carlo study of finite s le performance and an empirical ex le are included.
Publisher: Springer Science and Business Media LLC
Date: 06-01-2021
Publisher: Springer Science and Business Media LLC
Date: 07-10-2022
DOI: 10.1007/S11071-022-07927-0
Abstract: In functional time series analysis, the functional autocorrelation function (fACF) plays an important role in revealing the temporal dependence structures underlying the dynamics and identifying the lags at which substantial correlation exists. However, akin to its counterpart in the univariate case, the fACF is restricted by linear structure and can be misleading in reflecting nonlinear temporal dependence. This paper proposes a nonlinear alternative to the fACF for analyzing the temporal dependence in functional time series. We consider linear and nonlinear data generating processes: a functional autoregressive process and a functional generalized autoregressive conditional heteroskedasticity process. We demonstrate that when the process exhibits linear temporal structures, the inference obtained from our proposed nonlinear fACF is consistent with that from the fACF. When the underlying process exhibits nonlinear temporal dependence, our nonlinear fACF has a superior capability in uncovering the nonlinear structure that the fACF misleads. An empirical data analysis highlights its applications in unveiling nonlinear temporal structures in the daily curves of the intraday volatility dynamics of the foreign exchange rate.
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2023
End Date: 12-2025
Amount: $353,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2017
End Date: 06-2022
Amount: $409,500.00
Funder: Australian Research Council
View Funded Activity