A New Approximation for the Null Distribution of the Likelihood Ratio Test Statistics for k Upper Outliers in a Normal Sample

In this paper, we introduce a new approximation for the null distribution of the likelihood ratio test for the general case. We compare the the critical values obtained by the new approximation to the values which are obtained by the exact distribution for the cases k=1, 2 to test the accuracy of the new approximation. Also, we compare the results to another approximation method (which is known by Barnett and Lewis (1994)) for the cases k=3,4.Likelihood Ratio Test, Outlier, Approximation, Normal Sample

A New Approximation for the Null Distribution of the Likelihood Ratio Test Statistics for k Upper Outliers in a Normal Sample

In this paper, we introduce a new approximation for the null distribution of the likelihood ratio test for the general case. We compare the the critical values obtained by the new approximation to the values which are obtained by the exact distribution for the cases k=1, 2 to test the accuracy of the new approximation. Also, we compare the results to another approximation method (which is known by Barnett and Lewis (1994)) for the cases k=3,4

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

IS THE SAMPLE COEFFICIENT OF VARIATION A GOOD ESTIMATOR FOR THE POPULATION COEFFICIENT OF VARIATION?

In this paper, we obtain bounds for the population coefficient of variation (CV) in Bernoulli, Discrete Uniform, Normal and Exponential distributions. We also show that the sample coefficient of variation (cv) is not an accurate estimator of the population CV in the above indicated distributions. Finally we provide some suggestions based on the Maximum Likelihood Estimation to improve the population CV estimate

IS THE SAMPLE COEFFICIENT OF VARIATION A GOOD ESTIMATOR FOR THE POPULATION COEFFICIENT OF VARIATION?

In this paper, we obtain bounds for the population coefficient of variation (CV) in Bernoulli, Discrete Uniform, Normal and Exponential distributions. We also show that the sample coefficient of variation (cv) is not an accurate estimator of the population CV in the above indicated distributions. Finally we provide some suggestions based on the Maximum Likelihood Estimation to improve the population CV estimate.Coefficient of Variation (CV); Estimator; Maximum Likelihood Estimation (MLE)

Generalized bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance

Frangos and Vrontos (2001) proposed an optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. In this paper, we introduce a generalized form of those obtained previously

Two new confidence intervals for the coefficient of variation in a normal distribution

In this article we introduce an approximately unbiased estimator for the population coefficient of variation, τ, in a normal distribution. The accuracy of this estimator is examined by several criteria. Using this estimator and its variance, two approximate confidence intervals for τ are introduced. The performance of the new confidence intervals is compared to those obtained by current methods.coefficient of variation, confidence interval, normal distribution,