Penalized regression models with autoregressive error terms

Penalized regression methods have recently gained enormous attention in statistics and the field of machine learning due to their ability of reducing the prediction error and identifying important variables at the same time. Numerous studies have been conducted for penalized regression, but most of them are limited to the case when the data are independently observed. In this paper, we study a variable selection problem in penalized regression models with autoregressive (AR) error terms. We consider three estimators, adaptive least absolute shrinkage and selection operator, bridge, and smoothly clipped absolute deviation, and propose a computational algorithm that enables us to select a relevant set of variables and also the order of AR error terms simultaneously. In addition, we provide their asymptotic properties such as consistency, selection consistency, and asymptotic normality. The performances of the three estimators are compared with one another using simulated and real examples.     

A Folk Theorem for Asynchronously Repeated Games

We prove a Folk Theorem for asynchronously repeated games in which the set of players who can move in period t, denoted by I_t, is a random variable whose distribution is a function of the past action choices of the players and the past realizations of I_τ's, τ=1, 2,…,t−1. We impose a condition, the finite periods of inaction (FPI) condition, which requires that the number of periods in which every player has at least one opportunity to move is bounded. Given the FPI condition together with the standard nonequivalent utilities (NEU) condition, we show that every feasible and strictly individually rational payoff vector can be supported as a subgame perfect equilibrium outcome of an asynchronously repeated game.     

Analyzing Disaster Loss Trends: A Comparison of Normalization Methodologies in South Korea

Increasing concern for climate change adaptation and disaster risk reduction is driving the need for more accurate and sophisticated tools of analysis to protect populations. Standards of analysis that can normalize measurements under various contexts are particularly valuable in the global arena of disaster management. One concern that may benefit from normalizing is the analysis of disaster loss trends. Previous studies have used a combination of inflation, wealth, and societal factors in their normalization of disaster loss methodologies. This study examines the various normalization methods in previous research and applies a selection of eight formulae to 50 years of disaster data in South Korea. The results show both decreasing and increasing trends in disaster damage losses based on the methods, but there are curious biases under the results that may be artifacts of Korea's unique experiences in economic development. The conclusion discusses how the case of Korea may help to clarify the optimal normalization methodology for other countries.     

The mediating effect of organizational commitment and employee empowerment: how transformational leadership impacts employee knowledge sharing intention

This study examined the underlying process through which transformational leaders provide an influence on employees’ knowledge sharing intention by concentrating on mediating roles of psychological empowerment and employees’ commitment to organizations. The direct and indirect impacts of transformational leadership on knowledge sharing were explored. In order to test the proposed hypotheses, structural equation modelling analysis was conducted with a sample of 426 full-time employees in South Korea. The results of this study showed a significant direct effect of transformational leadership on psychological empowerment and organizational commitment, which in turn had a significant influence on employees’ knowledge sharing intention. Transformational leadership had only an indirect effect on knowledge sharing intention among employees. These results bring out the importance of mediating roles of employee’s attitudes, especially organizational commitment, for promoting knowledge sharing intention among employees.     

Adolescent egocentrism and indoor tanning: is the relationship direct or mediated?

This paper explored how imaginary audience and personal fable ideations contribute to adolescent indoor tanning intentions directly and indirectly through the way they shape pro-tanning attitude and association with peers who use tanning beds. Five hundred and ninety-five male (n = 207) and female (n = 387) adolescents, ranging in age from 11 to 19 (M = 16.87; SD = 1.34) years completed a cross-sectional survey. Measures included imaginary audience, personal fable (three dimensions: invulnerability, uniqueness, and omnipotence), pro-tanning attitude, association with peers who use tanning beds, and tanning bed use intentions. Bootstrapping analyses documented that imaginary audience ideations are indirectly associated with indoor tanning intentions through the mediation of pro-tanning attitude and association with peers who use tanning beds. Of the personal fable ideations, only invulnerability ideation is indirectly associated with indoor tanning intentions through the mediation of association with peers who use tanning beds. Design and evaluation of interventions and campaigns to reduce indoor tanning must be targeted to adolescents varying in imaginary audience ideations differently.     

Effects of aging on Type II muscle fibers: a systematic review of the literature

The authors conducted a systematic review of the literature for scientific articles in selected databases to determine the effects of aging on Type II muscle fibers in human skeletal muscles. They found that aging of Type II muscle fibers is primarily associated with a loss of fibers and a decrease in fiber size. Morphological changes with increasing age particularly included Type II fiber grouping. There is conflicting evidence regarding the change of proportion of Type II fibers. Type II muscle fibers seem to play an important role in the aging process of human skeletal muscles. According to this literature review, loss of fibers, decrease in size, and fiber-type grouping represent major quantitative changes. Because the process of aging involves various complex phenomena such as fiber-type coexpression, however, it seems difficult to assign those changes solely to a specific fiber type.     

Spatial modeling of extreme rainfall in northeast Thailand

It is well recognized that the generalized extreme value (GEV) distribution is widely used for any extreme events. This notion is based on the study of discrete choice behavior; however, there is a limit for predicting the distribution at ungauged sites. Hence, there have been studies on spatial dependence within extreme events in continuous space using recorded observations. We model the annual maximum daily rainfall data consisting of 25 locations for the period from 1982 to 2013. The spatial GEV model that is established under observations is assumed to be mutually independent because there is no spatial dependency between the stations. Furthermore, we divide the region into two regions for a better model fit and identify the best model for each region. We show that the regional spatial GEV model reflects the spatial pattern well compared with the spatial GEV model over the entire region as the local GEV distribution. The advantage of spatial extreme modeling is that more robust return levels and some indices of extreme rainfall can be obtained for observed stations as well as for locations without observed data. Thus, the model helps to determine the effects and assessment of vulnerability due to heavy rainfall in northeast Thailand.     

Adaptive LASSO for linear regression models with ARMA-GARCH errors

The linear regression models with the autoregressive moving average (ARMA) errors (REGARMA models) are often considered, in order to reflect a serial correlation among observations. In this article, we focus on an adaptive least absolute shrinkage and selection operator (LASSO) (ALASSO) method for the variable selection of the REGARMA models and extend it to the linear regression models with the ARMA-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) errors (REGARMA-GARCH models). This attempt is an extension of the existing ALASSO method for the linear regression models with the AR errors (REGAR models) proposed by Wang et al. in 2007 Wang, H., Li, G., Tsai, C. (2007). Regression coefficient and autoregressive order shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 69:63–78. [Google Scholar]. New ALASSO algorithms are proposed to determine important predictors for the REGARMA and REGARMA-GARCH models. Finally, we provide the simulation results and real data analysis to illustrate our findings.