VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity

In this study, we investigate linear regression having both heteroskedasticity and collinearity problems. We discuss the properties related to the perturbation method. Important observations are summarized as theorems. We then prove the main result that states the heteroskedasticity-robust variances can be improved and that the resulting bias is minimized by using the matrix perturbation method. We analyze a practical example for validation of the method.     

Vine copula constructions of higher-dimensional dependent reliability systems

Copulas have proved to be very successful tools for the flexible modeling of dependence. Bivariate copulas have been deeply researched in recent years, while building higher-dimensional copulas is still recognized to be a difficult task. In this paper, we study the higher-dimensional dependent reliability systems using a type of decomposition called “vine,” by which a multivariate distribution can be decomposed into a cascade of bivariate copulas. Some equations of system reliability for parallel, series, and k-out-of-n systems are obtained and then decomposed based on C-vine and D-vine copulas. Finally, a shutdown system is considered to illustrate the results obtained in the paper.     

Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.     

Schematic saturated orthogonal arrays obtained by using the contractive replacement method

This paper presents a general method of constructing schematic saturated orthogonal arrays of strength two. We consider a class of mixed saturated orthogonal arrays produced from saturated symmetric orthogonal arrays by using the contractive replacement method, study the Hamming distances of the rows, construct their association schemes, and prove that they are schematic. Some examples are given to illustrate this construction method.     

An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves

We propose an adaptive functional autoregressive (AFAR) forecast model to predict electricity price curves. With time-varying operators, the AFAR model can be safely used in both stationary and nonstationary situations. A closed-form maximum likelihood (ML) estimator is derived under stationarity. The result is further extended for nonstationarity, where the time-dependent operators are adaptively estimated under local homogeneity. We provide theoretical results of the ML estimator and the adaptive estimator. Simulation study illustrates nice finite sample performance of the AFAR modeling. The AFAR model also exhibits a superior accuracy in the forecast exercise of the California electricity daily price curves compared to several alternatives.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

Weighted simulated integrated conditional moment tests for parametric conditional distributions of stationary time series processes

In this article, we propose a weighted simulated integrated conditional moment (WSICM) test of the validity of parametric specifications of conditional distribution models for stationary time series data, by combining the weighted integrated conditional moment (ICM) test of Bierens (1984 Bierens, H. J. (1984). Model specification testing of time series regressions. Journal of Econometrics 26:323–353.[Crossref], [Web of Science ®] , [Google Scholar]) for time series regression models with the simulated ICM test of Bierens and Wang (2012 Bierens, H. J., Wang, L. (2012). Integrated conditional moment tests for parametric conditional distributions. Econometric Theory 28:328–362.[Crossref], [Web of Science ®] , [Google Scholar]) of conditional distribution models for cross-section data. To the best of our knowledge, no other consistent test for parametric conditional time series distributions has been proposed yet in the literature, despite consistency claims made by some authors.     

Promoting healthy transition to college through mindfulness training with first-year college students: Pilot randomized controlled trial

Objective: Given the importance of developmental transitions on young adults' lives and the high rates of mental health issues among U.S. college students, first-year college students can be particularly vulnerable to stress and adversity. This pilot study evaluated the effectiveness and feasibility of mindfulness training aiming to promote first-year college students' health and wellbeing. Participants: 109 freshmen were recruited from residential halls (50% Caucasian, 66% female). Data collection was completed in November 2014. Methods: A randomized control trial was conducted utilizing the Learning to BREATHE (L2B) program, a universal mindfulness program adapted to match the developmental tasks of college transition. Results: Participation in the pilot intervention was associated with significant increase in students' life satisfaction, and significant decrease in depression and anxiety. Marginally significant decrease was found for sleep issues and alcohol consequences. Conclusions: Mindfulness-based programs may be an effective strategy to enhance a healthy transition into college.     

The Association Between the New Rural Cooperative Medical System and Health Care Seeking Behavior Among Middle-Aged and Older Chinese

The new rural cooperative medical system (NCMS) is the primary form of social insurance in rural China. This study aims to explore how the NCMS influences the health care seeking behaviors of middle-aged and older Chinese, considering the family and community contexts. A series of multi-level (three-level) models using data from the first wave of the China Health and Retirement Longitudinal Study (CHARLS) are used. We find that the presence of NCMS coverage has a statistically significant association with seeking inpatient and outpatient care but not physical checkups among middle-aged and older rural Chinese: Rural residents insured by NCMS were more likely to seek inpatient and outpatient care than people who were not insured. Other factors at the individual level (such as self-perceived health and number of doctor-diagnosed chronic diseases), the family level (such as living arrangements and household expenditures), and the community level (such as the presence of township hospitals within the community) are also significant predictors of health care seeking behaviors.     

Perceived Need for Home- and Community-Based Services: Experiences of Urban Chinese Older Adults With Functional Impairments

Guided by Cantor’s social care model, this study identified individual, family, and social support factors that influence urban older adults’ need for home- and community-based services, including medical and rehabilitation, instrumental care and support, and psychosocial services. The data were extracted from the Sample Survey on Aged Population in Urban/Rural China conducted by the China Research Center on Aging in 2006. Results from multiple logistic regression show that older adults’ need for medical and rehabilitation services is significantly related to instrumental activities of daily living, depression, not having filial children, friend support networks, and having a confidant. Older adults’ need for instrumental care and support is related to their educational attainment, financial strain, instrumental activities of daily living, not living with children, and friend support networks. Finally, older adults’ need for psychosocial services is significantly related to educational attainment, depression, not being married, friend support networks, and having a confidant. Implications for social service development are discussed.