Computation of an efficient and robust estimator in a semiparametric mixture model

In this article, we propose an efficient and robust estimation for the semiparametric mixture model that is a mixture of unknown location-shifted symmetric distributions. Our estimation is derived by minimizing the profile Hellinger distance (MPHD) between the model and a nonparametric density estimate. We propose a simple and efficient algorithm to find the proposed MPHD estimation. Monte Carlo simulation study is conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods. Based on our empirical studies, the newly proposed procedure works very competitively compared to the existing methods for normal component cases and much better for non-normal component cases. More importantly, the proposed procedure is robust when the data are contaminated with outlying observations. A real data application is also provided to illustrate the proposed estimation procedure.     

A Hellinger distance approach to MCMC diagnostics

Bayesian analysis often requires the researcher to employ Markov Chain Monte Carlo (MCMC) techniques to draw samples from a posterior distribution which in turn is used to make inferences. Currently, several approaches to determine convergence of the chain as well as sensitivities of the resulting inferences have been developed. This work develops a Hellinger distance approach to MCMC diagnostics. An approximation to the Hellinger distance between two distributions f and g based on sampling is introduced. This approximation is studied via simulation to determine the accuracy. A criterion for using this Hellinger distance for determining chain convergence is proposed as well as a criterion for sensitivity studies. These criteria are illustrated using a dataset concerning the Anguilla australis, an eel native to New Zealand.     

孔子思想再阐释——以《论语》中的“远”为考察中心

孔子的思想体系建立在他对个人与自然、个人与社会、个人与个人三种关系的理解之上。"远"作为这三种关系的一种描述方式,在孔子思想中发挥着不可忽视的作用。学界对《论语》中"远"关注较少,因而以"远"为线索梳理孔子思想不仅饶有趣味而且具有相当的理论意义。     

An approach to measure distance between compositional diet estimates containing essential zeros

For many applications involving compositional data, it is necessary to establish a valid measure of distance, yet when essential zeros are present traditional distance measures are problematic. In quantitative fatty acid signature analysis (QFASA), compositional diet estimates are produced that often contain many zeros. In order to test for a difference in diet between two populations of predators using the QFASA diet estimates, a legitimate measure of distance for use in the test statistic is necessary. Since ecologists using QFASA must first select the potential species of prey in the predator's diet, the chosen measure of distance should be such that the distance between samples does not decrease as the number of species considered increases, a property known in general as subcompositional coherence. In this paper we compare three measures of distance for compositional data capable of handling zeros, but not satisfying some of the well-accepted principles of compositional data analysis. For compositional diet estimates, the most relevant of these is the property of subcompositionally coherence and we show that this property may be approximately satisfied. Based on the results of a simulation study and an application to real-life QFASA diet estimates of grey seals, we recommend the chi-square measure of distance.     

Outlier detection in high-dimensional regression model

An outlier is defined as an observation that is significantly different from the others in its dataset. In high-dimensional regression analysis, datasets often contain a portion of outliers. It is important to identify and eliminate the outliers for fitting a model to a dataset. In this paper, a novel outlier detection method is proposed for high-dimensional regression problems. The leave-one-out idea is utilized to construct a novel outlier detection measure based on distance correlation, and then an outlier detection procedure is proposed. The proposed method enjoys several advantages. First, the outlier detection measure can be simply calculated, and the detection procedure works efficiently even for high-dimensional regression data. Moreover, it can deal with a general regression, which does not require specification of a linear regression model. Finally, simulation studies show that the proposed method behaves well for detecting outliers in high-dimensional regression model and performs better than some other competing methods.     

Diagnostics for quasi-likelihood nonlinear models

Quasi-likelihood nonlinear models (QLNMs) are an extension of generalized linear model and include a widen class of models as special cases. This article investigates some diagnostic methods in QLNMs. An equivalency between a case-deletion model and a mean-shift outlier model in QLNM is established. Two simulation study and a real dataset are used to illustrate the proposed diagnostic methods.     

CEOs’ temporal focus,firm strategic change,and performance: Insights from a paradox perspective

Recent research indicates that CEOs’ temporal focus (the degree to which individuals attend to the past, present, and future) is a critical predictor for strategic outcomes. Building on paradox theory and the attention-based view, we examine the implications of CEOs’ past and future focus for strategic change. Results from polynomial regression analysis reveal that CEOs who cognitively embrace both the past and the future at the same time engage more in strategic change. In addition, our results reveal that the positive strategic change−firm performance relationship is enhanced when CEOs’ past focus is high, whereas CEOs’ future focus mitigates the translation of strategic change into firm performance (when their past focus is low at the same time). In addition, supplemental analyses indicate that the impact of CEOs’ temporal focus turns out differently in stable and dynamic environments. Our study thus extends the literature on both individual’s temporal focus and strategic change.     

有效提高远程学习者自主学习的策略

本文通过对远程学习者自主学习的现状分析，提出相应的策略以帮助学员更有效地开展和提高远程自主学习。     

Usability Study of a Distance Continuing Education Website for Human Service Professionals

ABSTRACT

A usability test of a distance continuing education website for human service professionals is discussed. The website's purpose was to help meet the continuing education needs of human service professionals in a largely rural Midwestern state. The purpose of the usability evaluation was to assess the website's ease of use, efficiency, and user satisfaction in a representative sample of human service professionals. Users completed eleven basic search tasks with the site and report their impressions of the information presented. In addition, performance data including success, time on task, and efficiency were gathered. Implications of user satisfaction and performance data highlight the importance of stakeholder's involvement in the early stages of website design.     

Semiparametric regression methods for temporal processes subject to multiple sources of censoring

Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response-190 is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease. A general area of application involves a recurrent event with non-negligible duration (e.g., hospitalization and associated length of hospital stay) and subject to a terminating event (e.g., death). We propose a semiparametric multiplicative model for the process version of the probability of being alive and in the (transient) state of interest. Under the proposed methods, the regression parameter is estimated through a procedure that does not require estimating the baseline probability. Unlike the majority of process regression methods, the proposed methods accommodate multiple sources of censoring. In particular, we derive a computationally convenient variant of inverse probability of censoring weighting based on the additive hazards model. We show that the regression parameter estimator is asymptotically normal, and that the baseline probability function estimator converges to a Gaussian process. Simulations demonstrate that our estimators have good finite sample performance. We apply our method to national end-stage liver disease data. The Canadian Journal of Statistics 48: 222–237; 2020 © 2019 Statistical Society of Canada