• Title/Summary/Keyword: likelihood ratio statistics

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A Study on Analysis of Likelihood Principle and its Educational Implications (우도원리에 대한 분석과 그에 따른 교육적 시사점에 대한 연구)

  • Park, Sun Yong;Yoon, Hyoung Seok
    • The Mathematical Education
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    • v.55 no.2
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    • pp.193-208
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    • 2016
  • This study analyzes the likelihood principle and elicits an educational implication. As a result of analysis, this study shows that Frequentist and Bayesian interpret the principle differently by assigning different role to that principle from each other. While frequentist regards the principle as 'the principle forming a basis for statistical inference using the likelihood ratio' through considering the likelihood as a direct tool for statistical inference, Bayesian looks upon the principle as 'the principle providing a basis for statistical inference using the posterior probability' by looking at the likelihood as a means for updating. Despite this distinction between two methods of statistical inference, two statistics schools get clues to compromise in a regard of using frequency prior probability. According to this result, this study suggests the statistics education that is a help to building of students' critical eye by their comparing inferences based on likelihood and posterior probability in the learning and teaching of updating process from frequency prior probability to posterior probability.

Statistical Inference Concerning Local Dependence between Two Multinomial Populations

  • Oh, Myong-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.413-428
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    • 2003
  • If a restriction is imposed only to a (proper) subset of parameters of interest, we call it a local restriction. Statistical inference under a local restriction in multinomial setting is studied. The maximum likelihood estimation under a local restriction and likelihood ratio tests for and against a local restriction are discussed. A real data is analyzed for illustrative purpose.

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Test of Local Restriction on a Multinomial Parameter

  • Oh, Myongsik
    • Communications for Statistical Applications and Methods
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    • v.10 no.2
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    • pp.525-534
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    • 2003
  • If a restriction is imposed only to a (proper) subset of parameters of interest, we call it a local restriction. Statistical inference under a local restriction in multinomial setting is studied. The maximum likelihood estimation under a local restriction and likelihood ratio tests for and against a local restriction are discussed. A real data is analyzed for illustrative purpose.

Minimum Hellinger Distance Bsed Goodness-of-fit Tests in Normal Models: Empirical Approach

  • Dong Bin Jeong
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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    • pp.967-976
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    • 1999
  • In this paper we study the Hellinger distance based goodness-of-fit tests that are analogs of likelihood ratio tests. The minimum Hellinger distance estimator (MHDE) in normal models provides an excellent robust alternative to the usual maximum likelihood estimator. Our simulation results show that the Hellinger deviance test (Simpson 1989) based goodness-of-fit test is robust when data contain outliers. The proposed hellinger deviance test(Simpson 1989) is a more direcct method for obtaining robust inferences than an automated outlier screen method used before the likelihood ratio test data analysis.

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Reliability Estimation of Generalized Geometric Distribution

  • Abouammoh, A.M.;Alshangiti, A.M.
    • International Journal of Reliability and Applications
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    • v.9 no.1
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    • pp.31-52
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    • 2008
  • In this paper generalized version of the geometric distribution is introduced. This distribution can be considered as a two-parameter generalization of the discrete geometric distribution. The main statistical and reliability properties of this distribution are discussed. Two methods of estimation, namely maximum likelihood method and the method of moments are used to estimate the parameters of this distribution. Simulation is utilized to calculate these estimates and to study some of their properties. Also, asymptotic confidence limits are established for the maximum likelihood estimates. Finally, the appropriateness of this new distribution for a set of real data, compared with the geometric distribution, is shown by using the likelihood ratio test and the Kolmogorove-Smirnove test.

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A modification of McFadden's R2 for binary and ordinal response models

  • Ejike R. Ugba;Jan Gertheiss
    • Communications for Statistical Applications and Methods
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    • v.30 no.1
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    • pp.49-63
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    • 2023
  • A lot of studies on the summary measures of predictive strength of categorical response models consider the likelihood ratio index (LRI), also known as the McFadden-R2, a better option than many other measures. We propose a simple modification of the LRI that adjusts for the effect of the number of response categories on the measure and that also rescales its values, mimicking an underlying latent measure. The modified measure is applicable to both binary and ordinal response models fitted by maximum likelihood. Results from simulation studies and a real data example on the olfactory perception of boar taint show that the proposed measure outperforms most of the widely used goodness-of-fit measures for binary and ordinal models. The proposed R2 interestingly proves quite invariant to an increasing number of response categories of an ordinal model.

Likelihood ratio in estimating gamma distribution parameters

  • Rahman, Mezbahur;Muraduzzaman, S. M.
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.2
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    • pp.345-354
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    • 2010
  • The Gamma Distribution is widely used in Engineering and Industrial applications. Estimation of parameters is revisited in the two-parameter Gamma distribution. The parameters are estimated by minimizing the likelihood ratios. A comparative study between the method of moments, the maximum likelihood method, the method of product spacings, and minimization of three different likelihood ratios is performed using simulation. For the scale parameter, the maximum likelihood estimate performs better and for the shape parameter, the product spacings estimate performs better. Among the three likelihood ratio statistics considered, the Anderson-Darling statistic has inferior performance compared to the Cramer-von-Misses statistic and the Kolmogorov-Smirnov statistic.

A generalized likelihood ratio chart for monitoring type I right-censored Weibull lifetimes (제1형 우측중도절단된 와이블 수명자료를 모니터링하는 GLR 관리도)

  • Han, Sung Won;Lee, Jaeheon
    • The Korean Journal of Applied Statistics
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    • v.30 no.5
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    • pp.647-663
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    • 2017
  • Weibull distribution is a popular distribution for modeling lifetimes because it reflects the characteristics of failure adequately and it models either increasing or decreasing failure rates simply. It is a standard method of the lifetimes test to wait until all samples failed; however, censoring can occur due to some realistic limitations. In this paper, we propose a generalized likelihood ratio (GLR) chart to monitor changes in the scale parameter for type I right-censored Weibull lifetime data. We also compare the performance of the proposed GLR chart with two CUSUM charts proposed earlier using average run length (ARL). Simulation results show that the Weibull GLR chart is effective to detect a wide range of shift sizes when the shape parameter and sample size are large and the censoring rate is not too high.

Parametric Tests and Estimation of Mean Change in Discrete Distributions

  • Kim, Jae-Hee;Cheon, Soo-Young
    • Communications for Statistical Applications and Methods
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    • v.16 no.3
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    • pp.511-518
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    • 2009
  • We consider the problem of testing for change and estimating the unknown change-point in a sequence of time-ordered observations from the binomial and Poisson distributions. Including the likelihood ratio test, Gombay and Horvath (1990) tests are studied and the proposed change-point estimator is derived from their test statistic. A power study of tests and a comparison study of change-point estimators are done via simulation.

Statistical Inference for Peakedness Ordering Between Two Distributions

  • Oh, Myong-Sik
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.05a
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    • pp.109-114
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    • 2003
  • The concept of dispersion is intrinsic to the theory and practice of statistics. A formulation of the concept of dispersion can be obtained by comparing the probability of intervals centered about a location parameter, which is peakedness ordering introduced first by Birnbaum (1948). We consider statistical inference concerning peakedness ordering between two arbitrary distributions. We propose nonparametric maximum likelihood estimator of two distributions under peakedness ordering and a likelihood ratio test for equality of dispersion in the sense of peakedness ordering.

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