• Title/Summary/Keyword: Goodness-of-Fit

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Estimation of the Parameter of a Bernoulli Distribution Using a Balanced Loss Function

  • Farsipour, N.Sanjari;Asgharzadeh, A.
    • Communications for Statistical Applications and Methods
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    • v.9 no.3
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    • pp.889-898
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    • 2002
  • In decision theoretic estimation, the loss function usually emphasizes precision of estimation. However, one may have interest in goodness of fit of the overall model as well as precision of estimation. From this viewpoint, Zellner(1994) proposed the balanced loss function which takes account of both "goodness of fit" and "precision of estimation". This paper considers estimation of the parameter of a Bernoulli distribution using Zellner's(1994) balanced loss function. It is shown that the sample mean $\overline{X}$, is admissible. More general results, concerning the admissibility of estimators of the form $a\overline{X}+b$ are also presented. Finally, minimax estimators and some numerical results are given at the end of paper,at the end of paper.

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.

Application of Rasch Analysis to the Gross Motor Function Measure: A Preliminary Study (대동작 기능 평가도구(GMFM)의 Rasch분석)

  • Yi, Chung-Hwi;Park, So-Yeon
    • Physical Therapy Korea
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    • v.11 no.2
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    • pp.9-16
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    • 2004
  • The purpose of this study was to validate the Korean version of the Gross Motor Function Measure (GMFM) using Rasch analysis. The data was obtained from the assessments of 59 children with cerebral palsy in Korea and were applied to Rasch's rating scale model to estimate the difficulty and goodness-of-fit of each item. Rasch modeling helped us to identify 76 items from the original 88-item GMFM that form an unidimensional hierarchical scale to rearrange 76 items in order of difficulty. Reliability coefficients of the 88-item and 76-item GMFM were .99 and .99, respectively. In this preliminary report, the Korean version of GMFM seems to have significant validity and reliability. These results may be useful in assessment of gross motor functions in children with cerebral palsy.

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Regression Analysis of Longitudinal Data Based on M-estimates

  • Jung, Sin-Ho;Terry M. Therneau
    • Journal of the Korean Statistical Society
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    • v.29 no.2
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    • pp.201-217
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    • 2000
  • The method of generalized estimating equations (GEE) has become very popular for the analysis of longitudinal data. We extend this work to the use of M-estimators; the resultant regression estimates are robust to heavy tailed errors and to outliers. The proposed method does not require correct specification of the dependence structure between observation, and allows for heterogeneity of the error. However, an estimate of the dependence structure may be incorporated, and if it is correct this guarantees a higher efficiency for the regression estimators. A goodness-of-fit test for checking the adequacy of the assumed M-estimation regression model is also provided. Simulation studies are conducted to show the finite-sample performance of the new methods. The proposed methods are applied to a real-life data set.

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A bivariate extension of the Hosking and Wallis goodness-of-fit measure for regional distributions

  • Kjeldsen, Thomas Rodding;Prosdocimi, Ilaria
    • Proceedings of the Korea Water Resources Association Conference
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    • 2015.05a
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    • pp.239-239
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    • 2015
  • This study presents a bivariate extension of the goodness-of-fit measure for regional frequency distributions developed by Hosking and Wallis [1993] for use with the method of L-moments. Utilising the approximate joint normal distribution of the regional L-skewness and L-kurtosis, a graphical representation of the confidence region on the L-moment diagram can be constructed as an ellipsoid. Candidate distributions can then be accepted where the corresponding the oretical relationship between the L-skewness and L-kurtosis intersects the confidence region, and the chosen distribution would be the one that minimises the Mahalanobis distance measure. Based on a set of Monte Carlo simulations it is demonstrated that the new bivariate measure generally selects the true population distribution more frequently than the original method. An R-code implementation of the method is available for download free-of-charge from the GitHub code depository and will be demonstrated on a case study of annual maximum series of peak flow data from a homogeneous region in Italy.

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Robust Fuzzy Varying Coefficient Regression Analysis with Crisp Inputs and Gaussian Fuzzy Output

  • Yang, Zhihui;Yin, Yunqiang;Chen, Yizeng
    • Journal of Computing Science and Engineering
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    • v.7 no.4
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    • pp.263-271
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    • 2013
  • This study presents a fuzzy varying coefficient regression model after deleting the outliers to improve the feasibility and effectiveness of the fuzzy regression model. The objective of our methodology is to allow the fuzzy regression coefficients to vary with a covariate, and simultaneously avoid the impact of data contaminated by outliers. In this paper, fuzzy regression coefficients are represented by Gaussian fuzzy numbers. We also formulate suitable goodness of fit to evaluate the performance of the proposed methodology. An example is given to demonstrate the effectiveness of our methodology.

Case influence diagnostics for the significance of the linear regression model

  • Bae, Whasoo;Noh, Soyoung;Kim, Choongrak
    • Communications for Statistical Applications and Methods
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    • v.24 no.2
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    • pp.155-162
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    • 2017
  • In this paper we propose influence measures for two basic goodness-of-fit statistics, the coefficient of determination $R^2$ and test statistic F in the linear regression model using the deletion method. Some useful lemmas are provided. We also express the influence measures in terms of basic building blocks such as residual, leverage, and deviation that showed them as increasing function of residuals and a decreasing function of deviation. Further, the proposed measure reduces computational burden from O(n) to O(1). As illustrative examples, we applied the proposed measures to the stackloss data sets. We verified that deletion of one or few influential observations may result in big change in $R^2$ and F-statistic.

Testing Whether a Survival Distribution is Better Mean Residual Life at Age $t_0$

  • Alwasel Ibrahim A.;El-Bassiouny Ahmed H.
    • International Journal of Reliability and Applications
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    • v.7 no.1
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    • pp.1-11
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    • 2006
  • The better mean residual life at $t_0\;(BMRL-t_0)$ class of life distribution is introduced by Kulasekara and Park (1987). They proved that the $BMRL-t_0$ class contains the DMRL class, but it is a proper subclass of the NBUE class. In this paper we develop a new family of tests for testing exponentiality against the $BMRL-t_0\;(WMRL-t_0)$ alternatives based on the goodness of fit approach. It is shown that the suggested test is better than the one introduced by Kulasekara and Park (1987) in the sense of Pitman asymptotic efficiency values.

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Goodness-of-fit tests for the inverse Weibull or extreme value distribution based on multiply type-II censored samples

  • Kang, Suk-Bok;Han, Jun-Tae;Seo, Yeon-Ju;Jeong, Jina
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.4
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    • pp.903-914
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    • 2014
  • The inverse Weibull distribution has been proposed as a model in the analysis of life testing data. Also, inverse Weibull distribution has been recently derived as a suitable model to describe degradation phenomena of mechanical components such as the dynamic components (pistons, crankshaft, etc.) of diesel engines. In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the shape parameter in the inverse Weibull distribution under multiply type-II censoring. We also develop four modified empirical distribution function (EDF) type tests for the inverse Weibull or extreme value distribution based on multiply type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

Asymptotic Relative Efficiency of Chi-squared Type Tests Based on the Empirical Process

  • Lee, Sang-Yeol
    • Journal of the Korean Statistical Society
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    • v.25 no.3
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    • pp.337-346
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    • 1996
  • The chi-squared type statistic generated from the empirical process can be used for testing the goodness of fit hypothesis on iid random sample. Lee (1995) showed that under some conditions, the chi-squared type statistic is asymptotically maximin in the sense of Strasser (1985). Since the chi-squared type statistic depends on the choice of *points in the unit interval, it is worth investigating the points yielding more efficient tests. Motivated by this viewpoint, we are led to study the asymptotic relative efficiency of chi-squared type tests in the same setting of Lee (1995). Some examples are given for illustration.

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