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Y. Wu
Department of Mathematics and Statistics, York University, Toronto, Canada

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Original article
Published: 02 March 2021 in Journal of Statistical Theory and Practice
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In this paper, we propose an empirical-characteristic-function-based change-point test for detecting changes in distributions. This test is not only fast and efficient for data of large dimension, but also very powerful as shown in simulation studies. We derive the null limiting distribution of the proposed test statistic that is a function of the average of squared independent Brownian bridges, which can be approximated by a normal distribution, or by simulations otherwise. We also establish the consistency of the test under weak conditions. We present a bi-segmentation procedure with use of the proposed test for multiple change-point analysis. We examine the finite sample performance of the proposed method using Monte Carlo simulations and a data example.

ACS Style

Xiaoping Shi; Yuehua Wu. An Empirical-Characteristic-Function-Based Change-Point Test for Detection of Multiple Distributional Changes. Journal of Statistical Theory and Practice 2021, 15, 1 -16.

AMA Style

Xiaoping Shi, Yuehua Wu. An Empirical-Characteristic-Function-Based Change-Point Test for Detection of Multiple Distributional Changes. Journal of Statistical Theory and Practice. 2021; 15 (2):1-16.

Chicago/Turabian Style

Xiaoping Shi; Yuehua Wu. 2021. "An Empirical-Characteristic-Function-Based Change-Point Test for Detection of Multiple Distributional Changes." Journal of Statistical Theory and Practice 15, no. 2: 1-16.

Article
Published: 12 October 2020 in Annals of the Institute of Statistical Mathematics
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In this paper, we propose a non-negative feature selection/feature grouping (nnFSG) method for general sign-constrained high-dimensional regression problems that allows regression coefficients to be disjointly homogeneous, with sparsity as a special case. To solve the resulting non-convex optimization problem, we provide an algorithm that incorporates the difference of convex programming, augmented Lagrange and coordinate descent methods. Furthermore, we show that the aforementioned nnFSG method recovers the oracle estimate consistently, and that the mean-squared errors are bounded. Additionally, we examine the performance of our method using finite sample simulations and applying it to a real protein mass spectrum dataset.

ACS Style

Shanshan Qin; Hao Ding; Yuehua Wu; Feng Liu. High-dimensional sign-constrained feature selection and grouping. Annals of the Institute of Statistical Mathematics 2020, 73, 787 -819.

AMA Style

Shanshan Qin, Hao Ding, Yuehua Wu, Feng Liu. High-dimensional sign-constrained feature selection and grouping. Annals of the Institute of Statistical Mathematics. 2020; 73 (4):787-819.

Chicago/Turabian Style

Shanshan Qin; Hao Ding; Yuehua Wu; Feng Liu. 2020. "High-dimensional sign-constrained feature selection and grouping." Annals of the Institute of Statistical Mathematics 73, no. 4: 787-819.

Journal article
Published: 22 July 2020 in Entropy
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In this third and final paper of our series on the topic of portfolio optimization, we introduce a further generalized portfolio selection method called generalized entropic portfolio optimization (GEPO). GEPO extends discrete entropic portfolio optimization (DEPO) to include intervals of continuous returns, with direct application to a wide range of option strategies. This lays the groundwork for an adaptable optimization framework that can accommodate a wealth of option portfolios, including popular strategies such as covered calls, married puts, credit spreads, straddles, strangles, butterfly spreads, and even iron condors. These option strategies exhibit mixed returns: a combination of discrete and continuous returns with performance best measured by portfolio growth rate, making entropic portfolio optimization an ideal method for option portfolio selection. GEPO provides the mathematical tools to select efficient option portfolios based on their growth rate and relative entropy. We provide an example of GEPO applied to real market option portfolio selection and demonstrate how GEPO outperforms traditional Kelly criterion strategies.

ACS Style

Peter Joseph Mercurio; Yuehua Wu; Hong Xie. Option Portfolio Selection with Generalized Entropic Portfolio Optimization. Entropy 2020, 22, 805 .

AMA Style

Peter Joseph Mercurio, Yuehua Wu, Hong Xie. Option Portfolio Selection with Generalized Entropic Portfolio Optimization. Entropy. 2020; 22 (8):805.

Chicago/Turabian Style

Peter Joseph Mercurio; Yuehua Wu; Hong Xie. 2020. "Option Portfolio Selection with Generalized Entropic Portfolio Optimization." Entropy 22, no. 8: 805.

Journal article
Published: 09 July 2020 in Entropy
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The portfolio optimization problem generally refers to creating an investment portfolio or asset allocation that achieves an optimal balance of expected risk and return. These portfolio returns are traditionally assumed to be continuous random variables. In An Entropy-Based Approach to Portfolio Optimization, we introduced a novel non-parametric optimization method based on Shannon entropy, called return-entropy portfolio optimization (REPO), which offers a simple and fast optimization algorithm for assets with continuous returns. Here, in this paper, we would like to extend the REPO approach to the optimization problem for assets with discrete distributed returns, such as those from a Bernoulli distribution like binary options. Under a discrete probability distribution, portfolios of binary options can be viewed as repeated short-term investments with an optimal buy/sell strategy or general betting strategy. Upon the outcome of each contract, the portfolio incurs a profit (success) or loss (failure). This is similar to a series of gambling wagers. Portfolio selection under this setting can be formulated as a new optimization problem called discrete entropic portfolio optimization (DEPO). DEPO creates optimal portfolios for discrete return assets based on expected growth rate and relative entropy. We show how a portfolio of binary options provides an ideal general setting for this kind of portfolio selection. As an example we apply DEPO to a portfolio of short-term foreign exchange currency pair binary options from the NADEX exchange platform and show how it outperforms leading Kelly criterion strategies. We also provide an additional example of a gambling application using a portfolio of sports bets over the course of an NFL season and present the advantages of DEPO over competing Kelly criterion strategies.

ACS Style

Peter Joseph Mercurio; Yuehua Wu; Hong Xie. Portfolio Optimization for Binary Options Based on Relative Entropy. Entropy 2020, 22, 752 .

AMA Style

Peter Joseph Mercurio, Yuehua Wu, Hong Xie. Portfolio Optimization for Binary Options Based on Relative Entropy. Entropy. 2020; 22 (7):752.

Chicago/Turabian Style

Peter Joseph Mercurio; Yuehua Wu; Hong Xie. 2020. "Portfolio Optimization for Binary Options Based on Relative Entropy." Entropy 22, no. 7: 752.

Chapter
Published: 23 May 2020 in Contemporary Experimental Design, Multivariate Analysis and Data Mining
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In this paper, the problem of multiple change points estimation is considered for generalized linear models, in which both the number of change-points and their locations are unknown. The proposed method is to first partition the data sequence into segments to construct a new design matrix, secondly convert the multiple change points estimation problem into a variable selection problem, and then apply a regularized model selection technique and obtain the regression coefficient estimation. The consistency of the estimator is established regardless if there is a change point in which the number of coefficients can diverge as the sample size goes to infinity. An algorithm is provided to estimate the multiple change points. Simulation studies are conducted for the logistic and log-linear models. A real data application is also presented.

ACS Style

Xiaoying Sun; Yuehua Wu. Simultaneous Multiple Change Points Estimation in Generalized Linear Models. Contemporary Experimental Design, Multivariate Analysis and Data Mining 2020, 341 -356.

AMA Style

Xiaoying Sun, Yuehua Wu. Simultaneous Multiple Change Points Estimation in Generalized Linear Models. Contemporary Experimental Design, Multivariate Analysis and Data Mining. 2020; ():341-356.

Chicago/Turabian Style

Xiaoying Sun; Yuehua Wu. 2020. "Simultaneous Multiple Change Points Estimation in Generalized Linear Models." Contemporary Experimental Design, Multivariate Analysis and Data Mining , no. : 341-356.

Journal article
Published: 14 March 2020 in Entropy
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This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with the mean-variance portfolio optimization (MVPO). Pioneered by Harry Markowitz, MVPO revolutionized the financial industry as the first formal mathematical approach to risk-averse investing. REPO uses a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. REPO and MVPO were compared by emulating competing portfolios over historical data and REPO significantly outperformed MVPO in a strong majority of cases.

ACS Style

Peter Joseph Mercurio; Yuehua Wu; Hong Xie. An Entropy-Based Approach to Portfolio Optimization. Entropy 2020, 22, 332 .

AMA Style

Peter Joseph Mercurio, Yuehua Wu, Hong Xie. An Entropy-Based Approach to Portfolio Optimization. Entropy. 2020; 22 (3):332.

Chicago/Turabian Style

Peter Joseph Mercurio; Yuehua Wu; Hong Xie. 2020. "An Entropy-Based Approach to Portfolio Optimization." Entropy 22, no. 3: 332.

Journal article
Published: 24 February 2020 in Proceedings of the National Academy of Sciences
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Commonly used methods for estimating parameters of a spatial dynamic panel data model include the two-stage least squares, quasi-maximum likelihood, and generalized moments. In this paper, we present an approach that uses the eigenvalues and eigenvectors of a spatial weight matrix to directly construct consistent least-squares estimators of parameters of a general spatial dynamic panel data model. The proposed methodology is conceptually simple and efficient and can be easily implemented. We show that the proposed parameter estimators are consistent and asymptotically normally distributed under mild conditions. We demonstrate the superior performance of our approach via extensive simulation studies. We also provide a real data example.

ACS Style

Baisuo Jin; Yuehua Wu; Calyampudi Radhakrishna Rao; Li Hou. Estimation and model selection in general spatial dynamic panel data models. Proceedings of the National Academy of Sciences 2020, 117, 5235 -5241.

AMA Style

Baisuo Jin, Yuehua Wu, Calyampudi Radhakrishna Rao, Li Hou. Estimation and model selection in general spatial dynamic panel data models. Proceedings of the National Academy of Sciences. 2020; 117 (10):5235-5241.

Chicago/Turabian Style

Baisuo Jin; Yuehua Wu; Calyampudi Radhakrishna Rao; Li Hou. 2020. "Estimation and model selection in general spatial dynamic panel data models." Proceedings of the National Academy of Sciences 117, no. 10: 5235-5241.

Journal article
Published: 23 February 2020 in Computational Statistics & Data Analysis
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Matching quantiles estimation (MQE) is a useful technique that allows one to find a linear combination of a set of random variables that matches the distribution of a target random variable. Since it is based on ordinary least-squares (OLS), it may be sensitive to outlier observations of the target random variable. A general matching quantiles M-estimation (MQME) method is thus proposed, which is resistant to outlier observations of the target random variable. Given that in most applications, the number of variables p may be large, a ‘sparse’ representation is highly desirable. The MQME is combined with the adaptive Lasso penalty so it can select informative variables. An iterative algorithm based on M-estimation is developed to compute MQME. The proposed matching quantiles M-estimate is consistent, just like the MQE. Extensive simulations are provided, in which efficient finite-sample performance of the new method is demonstrated. In addition, an illustrative real case study is presented.

ACS Style

Shanshan Qin; Yuehua Wu. General matching quantiles M-estimation. Computational Statistics & Data Analysis 2020, 147, 106941 .

AMA Style

Shanshan Qin, Yuehua Wu. General matching quantiles M-estimation. Computational Statistics & Data Analysis. 2020; 147 ():106941.

Chicago/Turabian Style

Shanshan Qin; Yuehua Wu. 2020. "General matching quantiles M-estimation." Computational Statistics & Data Analysis 147, no. : 106941.

Articles
Published: 03 February 2020 in Journal of Statistical Computation and Simulation
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During the past 30 years, many statistics have been proposed for tackling change-point problems. However, the null distributions of these statistics are not easy to obtain in both theory and practice. In this paper, we consider to use the beta distribution to approximate a standard distribution by matching their first four moments. Sufficient conditions are derived for the beta approximation to any distribution. The L∞ distance is used to evaluate the beta approximation to a distribution. To demonstrate the advantage of this approximation over the Rayleigh approximation, the Gumbel approximation and the empirical saddlepoint approximation in change-point analysis, three applications and one real data example are provided for illustration.

ACS Style

Shu Ding; Xiaoping Shi; Yuehua Wu. Beta approximation and its applications. Journal of Statistical Computation and Simulation 2020, 90, 1251 -1266.

AMA Style

Shu Ding, Xiaoping Shi, Yuehua Wu. Beta approximation and its applications. Journal of Statistical Computation and Simulation. 2020; 90 (7):1251-1266.

Chicago/Turabian Style

Shu Ding; Xiaoping Shi; Yuehua Wu. 2020. "Beta approximation and its applications." Journal of Statistical Computation and Simulation 90, no. 7: 1251-1266.

Articles
Published: 04 January 2020 in Journal of Statistical Computation and Simulation
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Movements of equity indices are very important information for an investment decision. Empirical studies illustrate that the movements switch among different regimes. The Markov regime-switching model has important applications to such analysis. However, parameters estimated under normality assumption might not be stable and the corresponding change-point detection algorithm might face some challenges when either the error distribution is heavy-tailed or observed data contain outliers. In this paper, we relax the normality assumption and propose a generalized Markov regime-switching (GMRS) model. We propose a GMRS model based change-point detection algorithm, which is tested on both simulation data and Hang Seng monthly index. Simulation studies show that this algorithm can improve the accuracy of identifying change-points when either the error distribution is heavy-tailed or observed data contain outliers. It is also evident that the identified change-points on Hang Seng monthly index data match the observed market behaviours.

ACS Style

Yufeng Lin; Yuehua Wu; Xiaogang Wang; Hao Ding. A segmented generalized Markov regime-switching model with its application in financial time series data. Journal of Statistical Computation and Simulation 2020, 90, 839 -853.

AMA Style

Yufeng Lin, Yuehua Wu, Xiaogang Wang, Hao Ding. A segmented generalized Markov regime-switching model with its application in financial time series data. Journal of Statistical Computation and Simulation. 2020; 90 (5):839-853.

Chicago/Turabian Style

Yufeng Lin; Yuehua Wu; Xiaogang Wang; Hao Ding. 2020. "A segmented generalized Markov regime-switching model with its application in financial time series data." Journal of Statistical Computation and Simulation 90, no. 5: 839-853.

Article
Published: 10 September 2019 in Communications in Mathematics and Statistics
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An M-estimation of the parameters in an undamped exponential signal model was proposed in Wu and Tam (IEEE Trans Signal Process 49(2):373–380, 2001), and the estimation was shown to be consistent under mild assumptions. In this paper, the limiting distributions of the M-estimators are investigated. It is shown that they are asymptotically normally distributed under similar conditions as assumed in Wu and Tam (IEEE Trans Signal Process 49(2):373–380, 2001). In addition, a recursive algorithm for computing the M-estimators of frequencies is proposed, and the strong consistency of these estimators is established. Monte Carlo simulation studies using Huber’s \(\rho \) function are also provided.

ACS Style

Shu Ding; Yuehua Wu; Kwok-Wai Tam. Notes on M-Estimation in Exponential Signal Models. Communications in Mathematics and Statistics 2019, 9, 139 -151.

AMA Style

Shu Ding, Yuehua Wu, Kwok-Wai Tam. Notes on M-Estimation in Exponential Signal Models. Communications in Mathematics and Statistics. 2019; 9 (2):139-151.

Chicago/Turabian Style

Shu Ding; Yuehua Wu; Kwok-Wai Tam. 2019. "Notes on M-Estimation in Exponential Signal Models." Communications in Mathematics and Statistics 9, no. 2: 139-151.

Journal article
Published: 15 July 2019 in Studies in Economics and Finance
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Purpose The purpose of this paper is to analyze different behaviors between long-term options’ implied volatilities and realized volatilities. Design/methodology/approach This paper uses a widely adopted short interest rate model that describes a stochastic process of the short interest rate to capture interest rate risk. Price a long-term option by a system of two stochastic processes to capture both underlying asset and interest rate volatilities. Model capital charges according to the Basel III regulatory specified approach. S&P 500 index and relevant data are used to illustrate how the proposed model works. Coup with the low interest rate scenario by first choosing an optimal time segment obtained by a multiple change-point detection method, and then using the data from the chosen time segment to estimate the CIR model parameters, and finally obtaining the final option price by incorporating the capital charge costs. Findings Monotonic increase in long-term option implied volatility can be explained mainly by interest rate risk, and the level of implied volatility can be explained by various valuation adjustments, particularly risk capital costs, which differ from existing published literatures that typically explained the differences in behaviors of long-term implied volatilities by the volatility of volatility or risk premium. The empirical results well explain long-term volatility behaviors. Research limitations/implications The authors only consider the market risk capital in this paper for demonstration purpose. Dealers may price the long-term options with the credit risk. It appears that other than the market risks such as underlying asset volatility and interest rate volatility, the market risk capital is a main nonmarket risk factor that significantly affects the long-term option prices. Practical implications Analysis helps readers and/or users of long-term options to understand why long-term option implied equity volatilities are much higher than observed. The framework offered in the paper provides some guidance if one would like to check if a long-term option is priced reasonable. Originality/value It is the first time to analyze mathematically long-term options’ volatility behavior in comparison with historically observed volatility.

ACS Style

Min Xu; Hong Xie; Yuehua Wu. Behavioral analysis of long-term implied volatilities. Studies in Economics and Finance 2019, 38, 583 -600.

AMA Style

Min Xu, Hong Xie, Yuehua Wu. Behavioral analysis of long-term implied volatilities. Studies in Economics and Finance. 2019; 38 (3):583-600.

Chicago/Turabian Style

Min Xu; Hong Xie; Yuehua Wu. 2019. "Behavioral analysis of long-term implied volatilities." Studies in Economics and Finance 38, no. 3: 583-600.

Articles
Published: 28 March 2019 in Communications in Statistics - Theory and Methods
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In this note, we present a theoretical result which relaxes a critical condition required by the semiparametric approach to dimension reduction. The asymptotic normality of the estimators still maintains under weaker assumptions. This improvement greatly increases the applicability of the semiparametric approach.

ACS Style

Bin Sun; Yuehua Wu; Wenzhi Yang; Yuejiao Fu. A note on the semiparametric approach to dimension reduction. Communications in Statistics - Theory and Methods 2019, 49, 2295 -2304.

AMA Style

Bin Sun, Yuehua Wu, Wenzhi Yang, Yuejiao Fu. A note on the semiparametric approach to dimension reduction. Communications in Statistics - Theory and Methods. 2019; 49 (9):2295-2304.

Chicago/Turabian Style

Bin Sun; Yuehua Wu; Wenzhi Yang; Yuejiao Fu. 2019. "A note on the semiparametric approach to dimension reduction." Communications in Statistics - Theory and Methods 49, no. 9: 2295-2304.

Journal article
Published: 15 March 2019 in Applied Mathematical Modelling
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Uncovering hidden change-points in an observed signal sequence is challenging both mathematically and computationally. We tackle this by developing an innovative methodology based on Markov chain Monte Carlo and statistical information theory. It consists of an empirical Bayesian information criterion (emBIC) to assess the fitness and virtue of candidate configurations of change-points, and a stochastic search algorithm induced from Gibbs sampling to find the optimal change-points configuration. Our emBIC is derived by treating the unknown change-point locations as latent data rather than parameters as is in traditional BIC, resulting in significant improvement over the latter which is known to mostly over-detect change-points. The use of the Gibbs sampler induced search enables one to quickly find the optimal change-points configuration with high probability and without going through computationally infeasible enumeration. We also integrate the Gibbs sampler induced search with a current BIC-based change-points sequential testing method, significantly improving the method’s performance and computing feasibility. We further develop two comprehensive 3-step computing procedures to implement the proposed methodology for practical use. Finally, simulation studies and real examples analyzing business and genetic data are presented to illustrate and assess the procedures.

ACS Style

Guoqi Qian; Yuehua Wu; Min Xu. Multiple change-points detection by empirical Bayesian information criteria and Gibbs sampling induced stochastic search. Applied Mathematical Modelling 2019, 72, 202 -216.

AMA Style

Guoqi Qian, Yuehua Wu, Min Xu. Multiple change-points detection by empirical Bayesian information criteria and Gibbs sampling induced stochastic search. Applied Mathematical Modelling. 2019; 72 ():202-216.

Chicago/Turabian Style

Guoqi Qian; Yuehua Wu; Min Xu. 2019. "Multiple change-points detection by empirical Bayesian information criteria and Gibbs sampling induced stochastic search." Applied Mathematical Modelling 72, no. : 202-216.

Articles
Published: 12 March 2019 in Communications in Statistics - Theory and Methods
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Three self-normalized two-sample test statistics are proposed for testing whether or not μi/σiv, i = 1, 2, are equal for a given v > 0, where μi and σi2 are the common mean and variance of the ith sample for i = 1, 2. They can be applied, but not limited to, the problems of the testing the equality of coefficients of variance when v = 1 and the equality of variance-to-mean ratio when v = 2. The self-normalized two-sample test statistics are distribution free and only dependent on the sample means and sample variances of both samples. It is shown that these test statistics are asymptotically normally distributed under the null hypothesis for each fixed v > 0. Simulation studies support the theoretical results. In addition, we illustrate the applicability of the proposed test by comparing students’ grades in seven semesters at a university in China and comparing the heights as well as weights of males to those of females, who have had their annual health examination at a health center in China.

ACS Style

Shu Ding; Baisuo Jin; Yuehua Wu; Jing Li; Baiqi Miao. Comparing ratios of the mean to a power of variance in two samples via self-normalized test statistics. Communications in Statistics - Theory and Methods 2019, 49, 2787 -2799.

AMA Style

Shu Ding, Baisuo Jin, Yuehua Wu, Jing Li, Baiqi Miao. Comparing ratios of the mean to a power of variance in two samples via self-normalized test statistics. Communications in Statistics - Theory and Methods. 2019; 49 (11):2787-2799.

Chicago/Turabian Style

Shu Ding; Baisuo Jin; Yuehua Wu; Jing Li; Baiqi Miao. 2019. "Comparing ratios of the mean to a power of variance in two samples via self-normalized test statistics." Communications in Statistics - Theory and Methods 49, no. 11: 2787-2799.

Conference paper
Published: 31 December 2018 in Quantitative Methods in Environmental and Climate Research
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This work improves the estimation algorithm of a general spatiotemporal autoregressive model proposed by Wu et al. (Br J Environ Clim Chang 7(4):223–235, 2017). We substitute their least squares technique in the EM-type algorithm by M-estimation and also present an M-estimation based change-point detection procedure. In addition, data examples are provided.

ACS Style

Bin Sun; Yuehua Wu. Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations. Quantitative Methods in Environmental and Climate Research 2018, 49 -62.

AMA Style

Bin Sun, Yuehua Wu. Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations. Quantitative Methods in Environmental and Climate Research. 2018; ():49-62.

Chicago/Turabian Style

Bin Sun; Yuehua Wu. 2018. "Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations." Quantitative Methods in Environmental and Climate Research , no. : 49-62.

Journal article
Published: 17 August 2018 in Journal of Applied Statistics
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ACS Style

M. Xu; Yuehua Wu; B. Jin. Detection of a change-point in variance by a weighted sum of powers of variances test. Journal of Applied Statistics 2018, 46, 664 -679.

AMA Style

M. Xu, Yuehua Wu, B. Jin. Detection of a change-point in variance by a weighted sum of powers of variances test. Journal of Applied Statistics. 2018; 46 (4):664-679.

Chicago/Turabian Style

M. Xu; Yuehua Wu; B. Jin. 2018. "Detection of a change-point in variance by a weighted sum of powers of variances test." Journal of Applied Statistics 46, no. 4: 664-679.

Journal article
Published: 21 May 2018 in Proceedings of the National Academy of Sciences
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The change-point detection has been carried out in terms of the Euclidean minimum spanning tree (MST) and shortest Hamiltonian path (SHP), with successful applications in the determination of authorship of a classic novel, the detection of change in a network over time, the detection of cell divisions, etc. However, these Euclidean graph-based tests may fail if a dataset contains random interferences. To solve this problem, we present a powerful non-Euclidean SHP-based test, which is consistent and distribution-free. The simulation shows that the test is more powerful than both Euclidean MST- and SHP-based tests and the non-Euclidean MST-based test. Its applicability in detecting both landing and departure times in video data of bees’ flower visits is illustrated.

ACS Style

Xiaoping Shi; Yuehua Wu; Calyampudi Radhakrishna Rao. Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data. Proceedings of the National Academy of Sciences 2018, 115, 5914 -5919.

AMA Style

Xiaoping Shi, Yuehua Wu, Calyampudi Radhakrishna Rao. Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data. Proceedings of the National Academy of Sciences. 2018; 115 (23):5914-5919.

Chicago/Turabian Style

Xiaoping Shi; Yuehua Wu; Calyampudi Radhakrishna Rao. 2018. "Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data." Proceedings of the National Academy of Sciences 115, no. 23: 5914-5919.

Journal article
Published: 16 December 2017 in British Journal of Environment and Climate Change
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ACS Style

Yuehua Wu; Xiaoying Sun; Elton Chan; Shanshan Qin. Detecting Non-negligible New Influences in Environmental Data via a General Spatio-temporal Autoregressive Model. British Journal of Environment and Climate Change 2017, 7, 223 -235.

AMA Style

Yuehua Wu, Xiaoying Sun, Elton Chan, Shanshan Qin. Detecting Non-negligible New Influences in Environmental Data via a General Spatio-temporal Autoregressive Model. British Journal of Environment and Climate Change. 2017; 7 (4):223-235.

Chicago/Turabian Style

Yuehua Wu; Xiaoying Sun; Elton Chan; Shanshan Qin. 2017. "Detecting Non-negligible New Influences in Environmental Data via a General Spatio-temporal Autoregressive Model." British Journal of Environment and Climate Change 7, no. 4: 223-235.

Journal article
Published: 20 June 2017 in Australian & New Zealand Journal of Statistics
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Spatially correlated data appear in many environmental studies, and consequently there is an increasing demand for estimation methods that take account of spatial correlation and thereby improve the accuracy of estimation. In this paper we propose an iterative nonparametric procedure for modelling spatial data with general correlation structures. The asymptotic normality of the proposed estimators is established under mild conditions. We demonstrate, using both simulation and case studies, that the proposed estimators are more efficient than the traditional locally linear methods which fail to account for spatial correlation.

ACS Style

Hongxia Wang; Yuehua Wu; Elton Chan. Efficient estimation of nonparametric spatial models with general correlation structures. Australian & New Zealand Journal of Statistics 2017, 59, 215 -233.

AMA Style

Hongxia Wang, Yuehua Wu, Elton Chan. Efficient estimation of nonparametric spatial models with general correlation structures. Australian & New Zealand Journal of Statistics. 2017; 59 (2):215-233.

Chicago/Turabian Style

Hongxia Wang; Yuehua Wu; Elton Chan. 2017. "Efficient estimation of nonparametric spatial models with general correlation structures." Australian & New Zealand Journal of Statistics 59, no. 2: 215-233.