A theoretical view of the envelope model for multivariate linear regression as response dimension reduction

The envelope model recently developed for the classical multivariate linear regression have potential gain in efficiency in estimating unknown parameters over usual maximum likelihood estimation. In this paper, we theoretically investigate the envelope model as dimension reduction for response variables and connect them to existing methods.     

The impact of dichotomization in longitudinal data analysis: a simulation study

In this paper, a simulation study is conducted to systematically investigate the impact of dichotomizing longitudinal continuous outcome variables under various types of missing data mechanisms. Generalized linear models (GLM) with standard generalized estimating equations (GEE) are widely used for longitudinal outcome analysis, but these semi‐parametric approaches are only valid under missing data completely at random (MCAR). Alternatively, weighted GEE (WGEE) and multiple imputation GEE (MI‐GEE) were developed to ensure validity under missing at random (MAR). Using a simulation study, the performance of standard GEE, WGEE and MI‐GEE on incomplete longitudinal dichotomized outcome analysis is evaluated. For comparisons, likelihood‐based linear mixed effects models (LMM) are used for incomplete longitudinal original continuous outcome analysis. Focusing on dichotomized outcome analysis, MI‐GEE with original continuous missing data imputation procedure provides well controlled test sizes and more stable power estimates compared with any other GEE‐based approaches. It is also shown that dichotomizing longitudinal continuous outcome will result in substantial loss of power compared with LMM. Copyright © 2009 John Wiley & Sons, Ltd.     

Chi-squared tests in kth-moment sufficient dimension reduction

We present a novel approach to sufficient dimension reduction for the conditional kth moments in regression. The approach provides a computationally feasible test for the dimension of the central kth-moment subspace. In addition, we can test predictor effects without assuming any models. All test statistics proposed in the novel approach have asymptotic chi-squared distributions.     

Joint Model of Clustered Failure Time Data with Informative Cluster Size

In this article, we propose an estimation procedure to estimate parameters of joint model when there exists a relationship between cluster size and clustered failure times of subunits within a cluster. We use a joint random effect model of clustered failure times and cluster size. To investigate the possible association, two submodels are connected by a common latent variable. The EM algorithm is applied for the estimation of parameters in the models. Simulation studies are performed to assess the finite sample properties of the estimators. Also, sensitivity tests show the influence of the misspecification of random effect distributions. The methods are applied to a lymphatic filariasis study for adult worm nests.     

An information system for Just-In-Time

In recent years, U.S.'s worldwide industrial and economic leadership has been seriously challenged by many foreign countries. In order to counter the problem, many U.S. manufacturing companies have sought effective materials management systems such as MRP and the ‘Just-In-Time’ (JIT) system as used by many Japanese companies. Even through many practitioners and academicians have been studying the JIT concept and have recommended ways of adapting the concept to the U.S. manufacturing environment, not very many organizations are enjoying benefits of JIT system in full scale because of misunderstanding of information needs in JIT system. The objectives of this paper are (1) to identify information requirements of the materials management systems under JIT concept, (2) to compare them against the requirements under MRP system, and (3) to propose an integrated management information system to support materials management system in JIT environment. The goal of this paper is to facilitate more effective implementation of the JIT system, and stimulate further research on the subject matter.     

Impact of missing data on type 1 error rates in non‐inferiority trials

In this paper, a simulation study is conducted to systematically investigate the impact of different types of missing data on six different statistical analyses: four different likelihood‐based linear mixed effects models and analysis of covariance (ANCOVA) using two different data sets, in non‐inferiority trial settings for the analysis of longitudinal continuous data. ANCOVA is valid when the missing data are completely at random. Likelihood‐based linear mixed effects model approaches are valid when the missing data are at random. Pattern‐mixture model (PMM) was developed to incorporate non‐random missing mechanism. Our simulations suggest that two linear mixed effects models using unstructured covariance matrix for within‐subject correlation with no random effects or first‐order autoregressive covariance matrix for within‐subject correlation with random coefficient effects provide well control of type 1 error (T1E) rate when the missing data are completely at random or at random. ANCOVA using last observation carried forward imputed data set is the worst method in terms of bias and T1E rate. PMM does not show much improvement on controlling T1E rate compared with other linear mixed effects models when the missing data are not at random but is markedly inferior when the missing data are at random. Copyright © 2009 John Wiley & Sons, Ltd.     

On the extension of sliced average variance estimation to multivariate regression

Many sufficient dimension reduction methods for univariate regression have been extended to multivariate regression. Sliced average variance estimation (SAVE) has the potential to recover more reductive information and recent development enables us to test the dimension and predictor effects with distributions commonly used in the literature. In this paper, we aim to extend the functionality of the SAVE to multivariate regression. Toward the goal, we propose three new methods. Numerical studies and real data analysis demonstrate that the proposed methods perform well.     

CEO Narcissism and Global Performance Variance in Multinational Enterprises: The Roles of Foreign Direct Investment Risk-Taking and Business Group Affiliation

This study examines key mechanisms through which CEO narcissism influences global performance variance in the context of Asian emerging market multinational enterprises. Building on the contextual reinforcement model of narcissism and the cushion hypothesis, we focus on the role of foreign direct investment (FDI) risk-taking and business group affiliation (BGA). We test our moderated mediation model on data from 149 South Korean multinational enterprises from 2006 to 2016. The results show that CEO narcissism is positively associated with FDI risk-taking. The effect of CEO narcissism on global performance variance is mediated by FDI risk-taking. Furthermore, BGA moderates the above-mentioned relationships. Our findings offer important contributions to the international business and CEO narcissism literatures.