@article {1969981, title = {Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk}, journal = {Biometrics}, volume = {71}, year = {2015}, month = {2015}, pages = {114-121}, abstract = {We propose a novel statistical framework by supplementing case-control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case-control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation. The proposed method is computationally simple and more efficient than standard approaches based on case-control data alone. We also establish asymptotic properties of the resulting estimator, and investigate its finite-sample performance through simulation. As a substantive application, we apply the proposed method to investigate risk factors for endometrial cancer, by using data from a recently completed population-based case-control study and summary statistics from the Behavioral Risk Factor Surveillance System, the Population Estimates Program of the US Census Bureau, and the Connecticut Department of Transportation.}, keywords = {Business Analytics}, author = {Chang,Xiaohui and Waagepetersen,R. and Yu,H. and Ma,X. and Holford,T. R. and Wang,R. and Guan,Y.} } @article {1969976, title = {Dynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform: New evidence from a two-state Markov-switching approach}, journal = {China Finance Review International}, volume = {5}, year = {2015}, month = {2015}, pages = {386-401}, abstract = {Purpose {\textendash} The purpose of this paper is to identify the bull and bear regimes in Chinese stock market and empirically analyze the dynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform.

Design/methodology/approach {\textendash} The authors investigate the price-volume relationship in the Chinese stock market before and after the Split Share Structure Reform using Shanghai Composite Index daily data from July 1994 to April 2013. Using a two-state Markov-switching autoregressive model and a modified two-state Markov-switching vector autoregression model, this study identifies bull or bear market and also examine the existence of regime-dependent Granger causality.

Findings {\textendash} Using a two-state Markov-switching autoregressive model, the authors detect structural changes in the market volatility due to the reform, and find evidence of a positive rather than an asymmetric price-volume contemporaneous correlation. There is a strong dynamic Granger causal relation from stock returns to trading volume before and after the reform regardless of the market conditions, but the causal effects of volume on returns are only seen in the bear markets before the reform. The model is robust when using different stock indices and time periods.

Originality/value {\textendash} The work is different from previous studies in the following aspects: most of the existing empirical literature focus on the well-developed economies, but our interest lies in the emerging Chinese market that has witnessed rapid growth in the past decade; in contrast to many works in the literature that examine the price-volume relationship during one market condition, the authors compare the relationship in a bull market with that in a bear market, using a two-state MS-AR model; the authors also employ a modified two-state Markov-switching vector autoregression model to examine the existence of regime-dependent Granger causality; as the most massive systematic reform for the Chinese stock market since its inception in 2005, the Split Share Structure Reform has a profound impact on the Chinese stock market, thus it is of vital importance to explore its effects on both the price-volume relationship and the market structure.}, keywords = {Business Analytics}, author = {Wang,Donghua and Lei,Man and Chang,Xiaohui} } @article {1969986, title = {Decorrelation Property of Discrete Wavelet Transform Under Fixed-Domain Asymptotics}, journal = {IEEE Transactions on Information Theory}, volume = {59}, year = {2013}, month = {2013}, pages = {8001-8013}, abstract = {Theoretical aspects of the decorrelation property of the discrete wavelet transform when applied to stochastic processes have been studied exclusively from the increasing-domain perspective, in which the distance between neighboring observations stays roughly constant as the number of observations increases. To understand the underlying data-generating process and to obtain good interpolations, fixed-domain asymptotics, in which the number of observations increases in a fixed region, is often more appropriate than increasing-domain asymptotics. In the fixed-domain setting, we prove that, for a general class of inhomogeneous covariance functions, with suitable choice of wavelet filters, the wavelet transform of a nonstationary process has mostly asymptotically uncorrelated components.}, keywords = {Business Analytics}, author = {Chang,Xiaohui and Stein,Michael L.} }