• Title/Summary/Keyword: null testing

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TESTING FOR SMOOTH TRANSITION NONLINEARITY IN PARTIALLY NONSTATIONARY VECTOR AUTOREGRESSIONS

  • Seo, Byeong-Seon
    • Journal of the Korean Statistical Society
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    • v.36 no.2
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    • pp.257-274
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    • 2007
  • This paper considers the tests for the presence of smooth transition non-linearity in the partially nonstationary vector autoregressive model. The transition parameters cannot be identified under the null hypothesis of linearity, and therefore this paper develops the tests for smooth transition nonlinearity, the associated asymptotic theory and the bootstrap inference. The Monte Carlo simulation evidence shows that the bootstrap inference generates moderate size and power performances.

Bootstrap tack of Fit Test based on the Linear Smoothers

  • Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.9 no.2
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    • pp.357-363
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    • 1998
  • In this paper we propose a nonparametric lack of fit test based on the bootstrap method for testing the null parametric linear model by using linear smoothers. Most of existing nonparametric test statistics are based on the residuals. Our test is based on the centered bootstrap residuals. Power performance of proposed bootstrap lack of fit test is investigated via Monte carlo simulation.

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Comparisons between Goodness-of-Fit Tests for ametric Model via Nonparametric Fit

  • Kim, Choon-Rak;Hong, Chan-Kon;Jeong, Mee-Seon
    • Communications for Statistical Applications and Methods
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    • v.3 no.3
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    • pp.39-46
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    • 1996
  • Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. In this paper we compare power of goodness-of-fit test statistics for testing the (null)parametric model versus the (alternative) nonparametric model.

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A Nonparametric Bootstrap Test and Estimation for Change

  • Kim, Jae-Hee
    • Communications for Statistical Applications and Methods
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    • v.14 no.2
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    • pp.443-457
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    • 2007
  • This paper deals with the problem of testing the existence of change in mean and estimating the change-point using nonparametric bootstrap technique. A test statistic using Gombay and Horvath (1990)'s functional form is applied to derive a test statistic and nonparametric change-point estimator with bootstrapping idea. Achieved significance level of the test is calculated for the proposed test to show the evidence against the null hypothesis. MSE and percentiles of the bootstrap change-point estimators are given to show the distribution of the proposed estimator in simulation.

A Test Procedure for Change in Level Occurring at Unknown Points

  • Lee, Jae-Chang;Song, Il-Seong
    • Journal of the Korean Statistical Society
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    • v.18 no.1
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    • pp.38-45
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    • 1989
  • A procedure is considered to the problem of testing whether there exist changes in location at possibly two points in a sequence of independent random variables which are successively drawn from normal population. A test statistics based on modified likelihood ratio is proposed and its asymptotic null distribution is derived through the stochastic process representation. A small sample power comparison is made by Monte Carlo method.

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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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An Adaptive Test for Ordered Interqartile Ranges among Several Distributions

  • Park, Chul-Gyu
    • Journal of the Korean Statistical Society
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    • v.30 no.1
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    • pp.63-76
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    • 2001
  • An adaptive estimation and testing method is proposed for comparing dispersions among several ordered groups. Based upon the large sampling theory for nonparametric quartile estimators, we derive the order restricted estimators and construct a simple test statistic. This test statistic has a mixture of several chi-square distributions as its asymptotic null distribution. The proposed test is illustratively applied to survival time data for the patients with carcinoma of the oropharynx.

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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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k-Sample Rank Procedures for Ordered Location-Scale Alternatives

  • Park, Hee-Moon
    • Journal of Korean Society for Quality Management
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    • v.22 no.2
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    • pp.166-176
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    • 1994
  • Some rank score tests are proposed for testing the equality of all sampling distribution functions against ordered location-scale alternatives in k-sample problem. Under the null hypothesis and a contiguous sequence of ordered location-scale alternatives, the asymptotic properties of the proposed test statistics are investigated. Also, the asymptotic local powers are compared with each others. The results show that the proposed tests based on the Hettmansperger-Norton type statistic are more powerful than others for the general ordered location-scale alternatives. However, the Shiraishi's tests based on the sum of two Bartholomew's rank analogue statistics are robust.

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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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