• Title/Summary/Keyword: null hypothesis

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A Study on the Analysis and Identification of Seafarers' Skill-Rule-Knowledge Inherent in Maritime Accidents

  • Yim, Jeong-bin
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.23 no.3
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    • pp.224-230
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    • 2017
  • The purpose of this study is to classify the deficient abilities of seafarers into SRK (Skill, Rule, and Knowledge) and analyze and identify the SRK by the type of accident and ship. Experimental data used the SRK cumulative frequency for 1,606 marine accident records and two-way ANOVA and t-test were used for the analysis tools. The results of two-way ANOVA showed that it is possible to identify the deficient abilities by using the cumulative frequency of SRK in both accident and ship types. As a result of the t-test, the adoption of the null hypothesis (H=0) that the mean of two pairs is equal and the rejection of the null hypothesis (H=1) were 29.2 % and 70.8 %, respectively. For the ship type, H=0 is 33.3 % and H=1 is 66.7 %. Through this study, it was found that about 70 % of the deficient abilities of seafarers inherent in maritime accidents can be identified using the proposed method.

A Study on Cell Influences to Chi-square Statistic in Contingency Tables

  • Kim, Hong-Gie
    • Communications for Statistical Applications and Methods
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    • v.5 no.1
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    • pp.35-42
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    • 1998
  • Once a contingency table is constructed, the first interest will be the hypotheses of either homogeneity or independence depending on the sampling scheme. The most widely used test statistic in practice is the classical Pearson's $\chi^2$ statistic. When the null hypothesis is rejected, another natural interest becomes which cell contributed to the rejection of the null hypothesis more than others. For this purpose, so called cell $\chi^2$ components are investigated. In this paper, the influence function of a cell to the $\chi^2$ statistic is derived, which can be used for the same purpose. This function measures the effect of each cell to the $\chi$$^2$ statistic. A numerical example is given to demonstrate the role of the new function.

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Improved non-parametric Model for Moving object segmentation by null hypothesis (귀무가설을 이용한 비모수 움직임 영상 검출 모델의 개선)

  • Lee, Ki-Sun;Na, Sang-Il;Lee, Jun-Woo;Jeong, Dong-Seok
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.249-250
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    • 2007
  • Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a improved non-parametric background model by null hypothesis. Evaluation shows that this approach achieves very sensitive detection with very low false alarm rates.

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ASYMPTPTIC DISTRIBUTION OF LIKELINOOD RATIO STATISTIC FOR TESTING MULTISAMPLE SPHERICITY

  • Gupta, A.K.;Nagar, D.K.;Jain, Kalpana
    • Journal of the Korean Statistical Society
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    • v.21 no.1
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    • pp.14-26
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    • 1992
  • In this paper, asymptotic expansions of the distribution of the likelihood ratio statistic for testing multisample sphericity have been derived in the null and nonnull cases when the alternatives are close to the null hypothesis. These expansions are obtained in the form of series of data distributions.

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Implementation of Statistical Significance and Practical Significance Using Research Hypothesis and Statistical Hypothesis in the Six Sigma Projects (식스시그마 프로젝트에서 연구가설과 통계가설에 의한 통계적 유의성 및 실무적 유의성의 적용방안)

  • Choi, Sung-Woon
    • Journal of the Korea Safety Management & Science
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    • v.15 no.1
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    • pp.283-292
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    • 2013
  • This paper aims to propose a new steps of hypothesis testing using analysis process and improvement process in the six sigma DMAIC. The six sigma implementation models proposed in this paper consist of six steps. The first step is to establish a research hypothesis by specification directionality and FBP(Falsibility By Popper). The second step is to translate the research hypothesis such as RHAT(Research Hypothesis Absent Type) and RHPT(Research Hypothesis Present Type) into statistical hypothesis such as $H_0$(Null Hypothesis) and $H_1$(Alternative Hypothesis). The third step is to implement statistical hypothesis testing by PBC(Proof By Contradiction) and proper sample size. The fourth step is to interpret the result of statistical hypothesis test. The fifth step is to establish the best conditions of product and process conditions by experimental optimization and interval estimation. The sixth step is to draw a conclusion by considering practical significance and statistical significance. Important for both quality practitioners and academicians, case analysis on six sigma projects with implementation guidelines are provided.

A Nonparametric Method for Nonlinear Regression Parameters

  • Kim, Hae-Kyung
    • Journal of the Korean Statistical Society
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    • v.18 no.1
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    • pp.46-61
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    • 1989
  • This paper is concerned with the development of a nonparametric procedure for the statistical inference about the nonlinear regression parameters. A confidence region and a hypothesis testing procedure based on a class of signed linear rank statistics are proposed and the asymptotic distributions of the test statistic both under the null hypothesis and under a sequence of local alternatives are investigated. Some desirable asymptotic properties including the asymptotic relative efficiency are discussed for various score functions.

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Hypothesis Testing for New Scores in a Linear Model

  • Park, Young-Hun
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.1007-1015
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    • 2003
  • In this paper we introduced a new score generating function for the rank dispersion function in a general linear model. Based on the new score function, we derived the null asymptotic theory of the rank-based hypothesis testing in a linear model. In essence we showed that several rank test statistics, which are primarily focused on our new score generating function and new dispersion function, are mainly distribution free and asymptotically converges to a chi-square distribution.

Computing Fractional Bayes Factor Using the Generalized Savage-Dickey Density Ratio

  • Younshik Chung;Lee, Sangjeen
    • Journal of the Korean Statistical Society
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    • v.27 no.4
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    • pp.385-396
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    • 1998
  • A computing method of fractional Bayes factor (FBF) for a point null hypothesis is explained. We propose alternative form of FBF that is the product of density ratio and a quantity using the generalized Savage-Dickey density ratio method. When it is difficult to compute the alternative form of FBF analytically, each term of the proposed form can be estimated by MCMC method. Finally, two examples are given.

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Inference on P(Y

  • Kim, Joong-Dae;Moon, Yeung-Gil;Kang, Jun-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.4
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    • pp.989-995
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    • 2003
  • Inference for probability P(Y

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Signed Linear Rank Statistics for Autoregressive Processes

  • Kim, Hae-Kyung;Kim, Il-Kyu
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.198-212
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    • 1995
  • This study provides a nonparametric procedure for the statistical inference of the parameters in stationary autoregressive processes. A confidence region and a hypothesis testing procedure based on a class of signed linear rank statistics are proposed and the asymptotic distributions of the test statistic both underthe null hypothesis and under a sequence of local alternatives are investigated. Some desirable asymptotic properties including the asymptotic relative efficiency are discussed for various score functions.

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