The influence of “country of origin” on multinational corporation global strategy: A conceptual framework

This article examines the proposition that MNCs from a particular country are likely to exhibit profile similarities that are distinct from those of MNCs emanating from another country due to differences in home country factors. We call this “country of origin effect” (COE). A generalized framework is presented briefly explaining the nature of relationships among various COE elements that influence MNC strategy. A number of research propositions are offered that postulate the presumed effect of COE elements on MNC strategy and competitive behavior. Finally, suggestions are made as to the implications of this avenue of enquiry for further research as well as guide for management action.     

Equivalence of two tests in varying coefficient partially linear errors in variable model with missing responses

This paper considers two tests on varying coefficient partially linear errors-in-variables models (VCPLM-EV) with missing responses under the linear constraint. The restricted estimator for the parametric component is derived and proven to share asymptotically normal distribution. In order to test the linear constraint, two statistics based on the profile Lagrange multiplier method and the corrected residual sum of squares method respectively, are proposed. It is of interest to obtain that the magnitudes of the two statistics are equal exactly and follow the asymptotical chi-square distribution. This reveals a new type of Wilk’s phenomenon in VCPLM-EV models with missing response. Finally, some numerical examples are carried out to illustrate relevant performances.     

Statistic inference for a single-index ARCH-M model

For a single-index autoregressive conditional heteroscedastic (ARCH-M) model, estimators of the parametric and non parametric components are proposed by the profile likelihood method. The research results had shown that all the estimators have consistency and the parametric estimators have asymptotic normality. We extend this line of research by deriving the asymptotic normality of the non parametric estimator. Based on the asymptotic properties, we propose Wald statistic and generalized likelihood ratio statistic to investigate the testing problems for ARCH effect and goodness of fit, respectively. A simulation study is conducted to evaluate the finite-sample performance of the proposed estimation methodology and testing procedure.     

Analyzing sub-population profiles and risk factors for school bullying

Drawing from a local sample of 1822 7th and 8th grade students, this study used Latent Class Analysis (LCA) and Latent Class Regression Analysis (LCRA) to identify sub-population profiles and risk factors for school bullying. Four sub-population profiles of school bullying risk were yielded from this approach. These profiles included students who presented little need for formal services as well as students who manifested needs for wrap-around support. Importantly, additional regression analyses related student membership in particular risk profile groups to the support they receive from peers, teachers, and parents. Several significant practice implications for bullying prevention and Response-to-Intervention (RTI) frameworks accompany the findings. Above all, school bullying interventions should be implemented with prudence because even the best intended ones carry the potential for harm.     

Optimal paired comparison designs for first-order interactions

In many fields of applications paired comparisons are used in which either full or partial profiles of the alternatives are presented. For this situation we introduce an appropriate model and derive optimal designs in the presence of interactions when all attributes have the same number of levels.     

Profile analysis of several groups

In this paper, profile analysis of several groups is considered. The maximum likelihood estimate, along with its covariance matrix, is given for the “level differences” between the grops. Likelihood ratio test procedures, with their distributions, are given for the three hypothesis known in the literature as “parallelism”, “level hypothesis” and “ no condition variation”. In the literature, even for the two groups case, some procedures are not likelihood procedures.     

On Maximum Likelihood Estimation for the Two Parameter Burr XII Distribution

This article considers three related aspects of maximum likelihood estimation of parameters in the two-parameter Burr XII distribution. Specifically, we first provide further clarification to some limiting results in Wingo (1993 Wingo , D. R. ( 1993 ). Maximum likelihood estimation of Burr XII distribution parameters under Type II censoring . Microelectron. Reliab. 33 : 1251 – 1257 .[Crossref], [Web of Science ®] , [Google Scholar]). We then focus on details in a proof of the uniqueness of the maximum likelihood estimators. Finally, we consider using the likelihood approach for data which does not satisfy Wingo's criterion, and show that this results in fitting either a Pareto distribution or an intuitively sensible degenerate distribution to the data. The discussion here is completely general, and not restricted to data obtained under Type II censoring.     

Statistical Inference in Partially Linear Varying-Coefficient Models with Missing Responses at Random

This article considers statistical inference for partially linear varying-coefficient models when the responses are missing at random. We propose a profile least-squares estimator for the parametric component with complete-case data and show that the resulting estimator is asymptotically normal. To avoid to estimate the asymptotic covariance in establishing confidence region of the parametric component with the normal-approximation method, we define an empirical likelihood based statistic and show that its limiting distribution is chi-squared distribution. Then, the confidence regions of the parametric component with asymptotically correct coverage probabilities can be constructed by the result. To check the validity of the linear constraints on the parametric component, we construct a modified generalized likelihood ratio test statistic and demonstrate that it follows asymptotically chi-squared distribution under the null hypothesis. Then, we extend the generalized likelihood ratio technique to the context of missing data. Finally, some simulations are conducted to illustrate the proposed methods.     

Profile Likelihood Based Confidence Intervals for Common Intraclass Correlation Coefficient

In this article, we propose an approach for estimating the confidence interval of the common intraclass correlation coefficient based on the profile likelihood. Comparisons are made with a procedure using the concept of generalized pivots. The method presented is less computationally demanding than the method using generalized pivots. The approach also provides better coverage, and shorter lengths of confidence intervals for the case when the value of the common intraclass correlation coefficient is low. The lengths of confidence intervals given by both methods are quite comparable for high but less realistic values of the common intraclass correlation coefficient.