Detecting Changes under Multivariate Normal Distributions via the Generalized Inference

It is commonly encountered in many fields to detect whether a change occurs on a population after a special process. Based on observations for describing the population before and after the process, we formulate this problem as two statistical hypotheses testing problems within a framework of multivariate statistical analysis and then propose a generalized inference approach to solve them. The corresponding generalized p values and their calculation details are provided. The proposed method is also extended to multiple testing problems. Simulation studies show that the proposed p values have satisfactory frequentist performance. We illustrate our methods with a real application in manufacturing of bearings that are used in medical devices.

Existing Approaches and Development Perspectives for Inferences

Statistical hypotheses testing is one of the basic direction of mathematical statistics the methods of which are widely used in theoretical research and practical applications. These methods are widely used in medical researches too. Scientists of different fields, among them of medical too, that are not experts in statistics, are often faced with the dilemma of which method to use for solving the problem they are interested. The article is devoted to helping the specialists in solving this problem and in finding the optimal resolution. For this purpose, here are very simple and clearly explained the essences of the existed approaches and are shown their positive and negative sides and are given the recommendations about their use depending on existed information and the aim that must be reached as a result of an investigation.

Time to take corporate innovation initiatives

Purpose A bad safety accident at a manufacturing company usually results in casualties and economic losses. The company affected by such an accident must deal with pressure from multiple stakeholder groups. Employees, in particular, play a key role in pushing the affected company to develop strategies to improve occupational safety and health. The purpose of this paper is to seek answers to two questions: does a safety accident affect employee behavior in terms of giving up prospects to develop a career at the affected company? If yes, could innovation initiatives adopted by the company help moderate the negative consequences from a safety accident? Design/methodology/approach By investigating 120 safety accidents reported by publicly listed Chinese manufacturing companies between 2009 and 2016, the authors conduct an empirical study using regression-based statistical hypotheses testing to describe the companies’ responses and prospects for their employees. Findings The results show that the magnitude of the accident and the accident being caused by an employee error positively affect the turnover of employees. Furthermore, technical innovation initiatives, such as spending on R&D, by the accident-affected companies increase the positive effect of the accident magnitude on employee turnover. On the contrary, management innovation initiatives, such as corporate social responsibility activities, weaken the impact of the accident magnitude and employee error on employee turnover. Originality/value This study contributes to knowledge development by adding a crisis perspective in human resource management research. It helps to better understand the impact of safety accidents on employee behavior and the response taken by companies through innovation initiatives.

Analysis of Nonparametric and Parametric Criteria for Statistical Hypotheses Testing. Chapter 1. Agreement Criteria of Pearson and Kolmogorov

In the statistical analysis of experimental results it is extremely important to know the distribution laws of the general population. ‎Because of all assumptions about the distribution laws are statistical hypotheses, they should be tested. ‎Testing hypotheses are carried out by using the statistical criteria that divided the multitude in two subsets: null and alternative. The ‎null hypothesis is accepted in subset null and is rejected in alternative subset. Knowledge of the distribution law is a prerequisite for the use of numerical mathematical methods. The hypothesis is accepted if the divergence between empirical and theoretical distributions will be random. The hypothesis is rejected if the divergence between empirical and theoretical distributions will be essential. There is a number of different agreement criteria for the statistical hypotheses testing. The paper continues ideas of the author’s works, devoted to advanced based tools of the mathematical statistics. This part of the paper is devoted to nonparametric agreement criteria. Nonparametric tests don’t allow us to include in calculations the parameters of the probability distribution and to operate with frequency only, as well as to assume directly that the experimental data have a specific distribution. Nonparametric criteria are widely used in analysis of the empirical data, in the testing of the simple and complex statistical hypotheses etc. They include the well known criteria of K. Pearson, A. Kolmogorov, N. H. Kuiper, G. S. Watson, T. W. Anderson, D. A. Darling, J. Zhang, Mann – Whitney U-test, Wilcoxon signed-rank test and so on. Pearson and Kolmogorov criteria are most frequently used in mathematical statistics. Pearson criterion (-criterion) is the universal statistical nonparametric criterion which has -distribution. It is used for the testing of the null hypothesis about subordination of the distribution of sample empirical to theory of general population at large amounts of sample (n>50). Pearson criterion is connected with calculation of theoretical frequency. Kolmogorov criterion is used for comparing empirical and theoretical distributions and permits to find the point in which the difference between these distributions is maximum and statistically reliable. Kolmogorov criterion is used at large amounts of sample too. It should be noted, that the results obtained by using Pearson criterion are more precise because practically all experimental data are used. The peculiarities of Pearson and Kolmogorov criteria are found out. The formulas for calculations are given and the typical tasks are suggested and solved. The typical tasks are suggested and solved that help us to understand more deeply the essence of Pearson and Kolmogorov criteria.

On Multiple Hypotheses LAO Testing With Rejection of Decision for Two Dependent Objects

Multiple statistical hypotheses testing with possibility of rejecting of decisionis considered for model consisting of two dependent objects characterized by joint discrete probability distribution. The matrix of error probabilities exponents (reliabilities) of asymptotically optimal tests is studied.

On Metaheuristics for Solving the Parameter Estimation Problem in Dynamic Systems: A Comparative Study

This paper presents an experimental study that aims to compare the practical performance of well-known metaheuristics for solving the parameter estimation problem in a dynamic systems context. The metaheuristics produce good quality approximations to the global solution of a finite small-dimensional nonlinear programming problem that emerges from the application of the sequential numerical direct method to the parameter estimation problem. Using statistical hypotheses testing, significant differences in the performance of the metaheuristics, in terms of the average objective function values and average CPU time, are determined. Furthermore, the best obtained solutions are graphically compared in relative terms by means of the performance profiles. The numerical comparisons with other results in the literature show that the tested metaheuristics are effective in achieving good quality solutions with a reduced computational effort.

Quantitative criteria for recognizing the incorrect behavior of computer network users

Two approaches for recognizing the incorrect behavior of computer network users are presented. The first one relies on the technique of statistical hypotheses testing and uses self-organizing feature maps (Kohonen networks) for generating target statistics. The second approach recognizes dangerous activity using executed sequences of relevant typical actions, with their dynamics being represented with the aid of Markov chains.

Applying configurational theory to build a typology of ethnocentric consumers

Purpose – Individuals showing high consumer ethnocentrism (CE) prefer domestic over foreign-made products and their preferences may contribute to barriers to international market entry. Therefore, how to identify such consumers is an important question. Shankarmahesh’s (2006) review reveals inconsistencies in the literature with regard to CE and its antecedents. To shed theoretical and empirical light on these inconsistencies, the purpose of this paper is to contribute two new perspectives on CE: first, a typology that classifies ethnocentric consumers by the extent to which they support government-controlled protectionism and consumer-controlled protectionism; and second, a configurational (recipe) perspective on the antecedents. Design/methodology/approach – The study applies fuzzy-set qualitative comparative analysis of survey data from 3,859 consumers. The study contrasts the findings with findings using traditional statistical hypotheses testing via multiple regression analysis. Findings – The results reveal several configurations of antecedents that are sufficient for consistently explaining three distinct types of CE. No single antecedent condition is necessary for high CE to occur. Practical implications – The findings help global business strategists in their market entry decisions and in their targeting and segmentation efforts. Originality/value – The authors show the value of asymmetrical thinking about the relationship between CE and its antecedents. The results expand understanding of CE and challenge conventional net-effects thinking about its antecedents.