• Title/Summary/Keyword: Statistical power of test

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Test for the Pareto Distribution Based on the Transformed Sample Lorenz Curve

  • Kang, Suk-Bok;Cho, Young-Suk
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.05a
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    • pp.133-137
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    • 2002
  • A powerful and easily computed goodness-of-fit test for Pareto distribution which does not depend on the unknown location and scale parameters is proposed based on the transformed sample Lorenz curve. We compare the power of the proposed test statistic with the other goodness-of-fit tests for Pareto distribution against various alternatives through Monte Carlo methods.

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Goodness-of-fit tests based on generalized Lorenz curve for progressively Type II censored data from a location-scale distributions

  • Lee, Wonhee;Lee, Kyeongjun
    • Communications for Statistical Applications and Methods
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    • v.26 no.2
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    • pp.191-203
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    • 2019
  • The problem of examining how well an assumed distribution fits the data of a sample is of significant and must be examined prior to any inferential process. The observed failure time data of items are often not wholly available in reliability and life-testing studies. Lowering the expense and period associated with tests is important in statistical tests with censored data. Goodness-of-fit tests for perfect data can no longer be used when the observed failure time data are progressive Type II censored (PC) data. Therefore, we propose goodness-of-fit test statistics and a graphical method based on generalized Lorenz curve for PC data from a location-scale distribution. The power of the proposed tests is then assessed through Monte Carlo simulations. Finally, we analyzed two real data set for illustrative purposes.

The Limit Distribution and Power of a Test for Bivariate Normality

  • Kim, Namhyun
    • Communications for Statistical Applications and Methods
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    • v.9 no.1
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    • pp.187-196
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    • 2002
  • Testing for normality has always been a center of practical and theoretical interest in statistical research. In this paper a test statistic for bivariate normality is proposed. The underlying idea is to investigate all the possible linear combinations that reduce to the standard normal distribution under the null hypothesis and compare the order statistics of them with the theoretical normal quantiles. The suggested statistic is invariant with respect to nonsingular matrix multiplication and vector addition. We show that the limit distribution of an approximation to the suggested statistic is represented as the supremum over an index set of the integral of a suitable Gaussian Process. We also simulate the null distribution of the statistic and give some critical values of the distribution and power results.

Nonparametric Tests for 2×2 Cross-Over Design

  • Gee, Kyuhoon;Kim, Dongjae
    • Communications for Statistical Applications and Methods
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    • v.19 no.6
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    • pp.781-791
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    • 2012
  • A $2{\times}2$ Cross-over design is widely used in clinical trials for comparison studies of two kinds of drugs or medical treatments. This design has many statistical methods such as Hills-Armitage's (1979) method or Koch's (1972) method. In this paper, we propose a nonparametric test for $2{\times}2$ Cross-over design based on a two-sample test suggested by Baumgartner et al. (1998). In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of previous methods.

Test and Estimation for Exponential Mean Change

  • Kim, Jae-Hee
    • Communications for Statistical Applications and Methods
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    • v.15 no.3
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    • pp.421-427
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    • 2008
  • This paper deals with the problem of testing for the existence of change in mean and estimating the change-point when the data are from the exponential distributions. The likelihood ratio test statistic and Gombay and Horvath (1990) test statistic are compared in a power study when there exists one change-point in the exponential means. Also the change-point estimator using the likelihood ratio and the change-point estimators based on Gombay and Horvath (1990) statistic are compared for their detecting capability via simulation.

Failure Analysis and Weibull Statistical Analysis according to Impact Test of the Angular Pin for Injection Molding Machines (사출금형기계용 앵귤러핀의 충격시험에 따른 파손분석과 와이블 통계 해석)

  • Kim, Cheol-Su;Nam, Ki-Woo;Ahn, Seok-Hwan
    • Journal of Power System Engineering
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    • v.21 no.3
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    • pp.37-44
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    • 2017
  • In this study, failure analysis of the angular pin for molding machines to aluminum component molding was carried out. SM45C steel was used for the angular pin, it was surface hardened by the induction surface hardening heat treatment. The cross section of damaged angular pin was observed, and micro Vickers hardness value from the fractured part was measured. Brittle fracture was occurred from the fracture surface of angular pin, therefore, impact toughness value was evaluated by V-notch Charpy impact test. It was confirmed that the impact absorption energy was high when was tempered at a high temperature for a long time, and the toughness was slightly increased. Also, 2-parameter Weibull statistical analysis was investigated in order to evaluate the reliability of the measured micro Vickers hardness values and absorbed energy. The micro Vickers hardness and absorbed energy well followed a two-parameter Weibull probability distribution, respectively. The reverse design against angular pin was proposed as possible by using test results.

A Review on the Use of Effect Size in Nursing Research (간호학 연구에서 효과크기의 사용에 대한 고찰)

  • Kang, Hyuncheol;Yeon, Kyupil;Han, Sang-Tae
    • Journal of Korean Academy of Nursing
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    • v.45 no.5
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    • pp.641-649
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    • 2015
  • Purpose: The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. Methods: For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. Results: Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. Conclusion: It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.

Controling the Healthy Worker Effect in Occupational Epidemiology

  • Kim, Jin-Heum;Nam, Chung-Mo
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.11a
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    • pp.197-201
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    • 2002
  • The healthy worker effect is an important issue in occupational epidemiology. We proposed a new statistical method to test the relationship between exposure and time to death in the presence of the healthy worker effect. In this study, we considered the healthy worker hire effect to operate as a confounder and the healthy worker survival effect to operate as a confounder and an intermediate variable. The basic idea of the proposed method reflects the length bias-sampling caused by changing one's employment status. Simulation studies were also carried out to compare the proposed method with the Cox proportional hazards models. According to our simulation studies, both the proposed test and the test based on the Cox model having the change of the employment status as a time-dependent covariate seem to be satisfactory at an upper 5% significance level. The Cox models, however, are inadequate with the change, if any, of the employment status as time-independent covariate. The proposed test is superior in power to the test based on the Cox model including the time-dependent employment status.

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Power Test of Trend Analysis using Simulation Experiment (모의실험을 이용한 경향성 분석기법의 검정력 평가)

  • Ryu, Yongjun;Shin, Hongjoon;Kim, Sooyoung;Heo, Jun-Haeng
    • Journal of Korea Water Resources Association
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    • v.46 no.3
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    • pp.219-227
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    • 2013
  • Time series data including change, jump, trend and periodicity generally have nonstationarity. Especially, various methods have been proposed to identify the trend about hydrological time series data. However, among various methods, evaluation about capability of each trend test has not been done a lot. Even for the same data, each method may show the different result. In this study, the simulation was performed for identification about the changes in trend analysis according to the statistical characteristics and the capability in the trend analysis. For this purpose, power test for the trend analysis is conducted using Men-Kendall test, Hotelling-Pabst test, t test and Sen test according to the slope, sample size, standard deviation and significance level. As a result, t test has higher statistical power than the others, while Mann-Kendall, Hotelling-Pabst, and Sen tests were similar results.

Goodness of Link Tests for Binary Response Data

  • Yeo, In-Kwon
    • Communications for Statistical Applications and Methods
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    • v.8 no.2
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    • pp.357-366
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    • 2001
  • The present paper develops a method to check the propriety of link functions for binary data. In order to parameterize a certain type of goodness of the link, a family of link functions indexed by a shape parameter is proposed. I first investigate the maximum likelihood estimation of the shape parameter as well as regression parameters and then derive their large sample behaviors of the estimators. A score test is considered to evaluate the goodness of the current link function. For illustration, I employ two families of power transformations, the modulus transformation by John and Draper (1980) and the extended power transformation by Yeo and Johnson (2000), which are appropriate to detect symmetric and asymmetric inadequacy of the selected link function. respectively.

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