• 제목/요약/키워드: Statistical testing

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Bayesian Hypothesis Testing in Multivariate Growth Curve Model.

  • Kim, Hea-Jung;Lee, Seung-Joo
    • Journal of the Korean Statistical Society
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    • 제25권1호
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    • pp.81-94
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    • 1996
  • This paper suggests a new criterion for testing the general linear hypothesis about coefficients in multivariate growth curve model. It is developed from a Bayesian point of view using the highest posterior density region methodology. Likelihood ratio test criterion(LRTC) by Khatri(1966) results as an approximate special case. It is shown that under the simple case of vague prior distribution for the multivariate normal parameters a LRTC-like criterion results; but the degrees of freedom are lower, so the suggested test criterion yields more conservative test than is warranted by the classical LRTC, a result analogous to that of Berger and Sellke(1987). Moreover, more general(non-vague) prior distributions will generate a richer class of tests than were previously available.

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The Cusum of Squares Test for Variance Changes in Infinite Order Autoregressive Models

  • Park, Siyun;Lee, Sangyeol;Jongwoo Jeon
    • Journal of the Korean Statistical Society
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    • 제29권3호
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    • pp.351-360
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    • 2000
  • This paper considers the problem of testing a variance change in infinite order autoregressive models. A cusum of squares test based on the residuals from an AR(q) model is constructed analogous to Inclan and Tiao (1994)'s test statistic, where q is a sequence of positive integers diverging to $\infty$. It is shown that under regularity conditions the limiting distribution of the test statistic is the sup of a standard Brownian bridge. Simulation results are given to illustrate the performance of the test.

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On Information Theoretic Index for Measuring the Stochastic Dependence Among Sets of Variates

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • 제26권1호
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    • pp.131-146
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    • 1997
  • In this paper the problem of measuring the stochastic dependence among sets fo random variates is considered, and attention is specifically directed to forming a single well-defined measure of the dependence among sets of normal variates. A new information theoretic measure of the dependence called dependence index (DI) is introduced and its several properties are studied. The development of DI is based on the generalization and normalization of the mutual information introduced by Kullback(1968). For data analysis, minimum cross entropy estimator of DI is suggested, and its asymptotic distribution is obtained for testing the existence of the dependence. Monte Carlo simulations demonstrate the performance of the estimator, and show that is is useful not only for evaluation of the dependence, but also for independent model testing.

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Testing of Stochastic Trends, Seasonal and Cyclical Components in Macroeconomil Time Series

  • Gil-Alana Luis A.
    • Communications for Statistical Applications and Methods
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    • 제12권1호
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    • pp.101-115
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    • 2005
  • We propose in this article a procedure for testing unit and fractional orders of integration, with the roots simultaneously occurring in the trend, the seasonal and the cyclical component of the time series. The tests have standard null and local limit distributions. However, finite sample critical values are computed, and several Monte Carlo experiments conducted across the paper show that the rejection frequencies against unit (and fractional) orders of integration are relatively high in all cases. The tests are applied to the UK consumption and income series, the results showing the importance of the roots corresponding to the trend and the seasonal components and, though the unit roots are found to be fairly suitable models, we show that fractional processes (including one for the cyclical component) may also be plausible alternatives in some cases.

Estimation of Coverage Growth Functions

  • Park, Joong-Yang;Lee, Gye-Min;Kim, Seo-Yeong
    • Communications for Statistical Applications and Methods
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    • 제18권5호
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    • pp.667-674
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    • 2011
  • A recent trend in software reliability engineering accounts for the coverage growth behavior during testing. The coverage growth function (representing the coverage growth behavior) has become an essential component of software reliability models. Application of a coverage growth function requires the estimation of the coverage growth function. This paper considers the problem of estimating the coverage growth function. The existing maximum likelihood method is reviewed and corrected. A method of minimizing the sum of squares of the standardized prediction error is proposed for situations where the maximum likelihood method is not applicable.

Point and interval estimation for a simple step-stress model with Type-I censored data from geometric distribution

  • Arefi, Ahmad;Razmkhah, Mostafa
    • Communications for Statistical Applications and Methods
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    • 제24권1호
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    • pp.29-41
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    • 2017
  • The estimation problem of expected time to failure of units is studied in a discrete set up. A simple step-stress accelerated life testing is considered with a Type-I censored sample from geometric distribution that is a commonly used distribution to model the lifetime of a device in discrete case. Maximum likelihood estimators as well as the associated distributions are derived. Exact, approximate and bootstrap approaches construct confidence intervals that are compared via a simulation study. Optimal confidence intervals are suggested in view of the expected width and coverage probability criteria. An illustrative example is also presented to explain the results of the paper. Finally, some conclusions are stated.

Influential Points in GLMs via Backwards Stepping

  • Jeong, Kwang-Mo;Oh, Hae-Young
    • Communications for Statistical Applications and Methods
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    • 제9권1호
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    • pp.197-212
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    • 2002
  • When assessing goodness-of-fit of a model, a small subset of deviating observations can give rise to a significant lack of fit. It is therefore important to identify such observations and to assess their effects on various aspects of analysis. A Cook's distance measure is usually used to detect influential observation. But it sometimes is not fully effective in identifying truly influential set of observations because there may exist masking or swamping effects. In this paper we confine our attention to influential subset In GLMs such as logistic regression models and loglinear models. We modify a backwards stepping algorithm, which was originally suggested for detecting outlying cells in contingency tables, to detect influential observations in GLMs. The algorithm consists of two steps, the identification step and the testing step. In identification step we Identify influential observations based on influencial measures such as Cook's distances. On the other hand in testing step we test the subset of identified observations to be significant or not Finally we explain the proposed method through two types of dataset related to logistic regression model and loglinear model, respectively.

통계적 가설검정으로서의 선별검사절차의 검토 (Review of Screening Procedure as Statistical Hypothesis Testing)

  • 권혁무;이민구;김상부;홍성훈
    • 품질경영학회지
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    • 제26권2호
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    • pp.39-50
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    • 1998
  • A screening procedure, where one or more correlated variables are used for screeing, is reviewed from the point of statistical hypothesis testing. Without assuming a specific probability model for the joint distribution of the performance and screening variables, some principles are provided to establish the best screeing region. A, pp.ication examples are provided for two cases; ⅰ) the case where the performance variable is dichotomous and ⅱ) the case where the performance variable is continuous. In case ⅰ), a normal model is assumed for the conditional distribution of the screening variable given the performance variable. In case ⅱ), the performance and screening variables are assumed to be jointly normally distributed.

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Comparison Density Representation of Traditional Test Statistics for the Equality of Two Population Proportions

  • Jangsun Baek
    • Communications for Statistical Applications and Methods
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    • 제2권1호
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    • pp.112-121
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    • 1995
  • Let $p_1$ and $p_2$ be the proportions of two populations. To test the hypothesis $H_0 : p_1 = p_2$, we usually use the $x^2$ statistic, the large sample binomial statistic Z, and the Generalized Likelihood Ratio statistic-2log $\lambda$developed based on different mathematical rationale, respectively. Since testing the above hypothesis is equivalent to testing whether two populations follow the common Bernoulli distribution, one may also test the hypothesis by comparing 1 with the ratio of each density estimate and the hypothesized common density estimate, called comparison density, which was devised by Parzen(1988). We show that the above traditional test statistics ate actually estimating the measure of distance between the true densities and the common density under $H_0$ by representing them with the comparison density.

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횔체어 시트쿠션의 접촉 압력 평가에 관한 연구 (The Study on the Evaluation of Contact Pressure of Wheelchair Seat Cushion)

  • 강영식;양성환;조문선;신유민
    • 대한안전경영과학회:학술대회논문집
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    • 대한안전경영과학회 2010년도 춘계학술대회
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    • pp.61-69
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    • 2010
  • The users who use the wheelchair are confined to a wheelchair for a long time. Accordingly, the use of seat cushion for pressure distribution is very important in order to prevent a bedsore. Therefore, this paper provides useful information for design of seat cushion through statistical testing among nothing cushion, low cell type of air cushion, high cell type of air cushion, and jelly type of air cushion. It turned out that the jelly type and high cell type of air cushion have a serious effect on decision and design of seat cushion.

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