Now showing items 1-12 of 12

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    A permutation test for partial regression coefficients on first-order autocorrelation 

    Pradthana Minsan; Pachitjanut Siripanich, advisor (National Institute of Development Administration, 2010)

    This dissertation proposes a permutation test and a permutation procedure for testing on partial regression coefficients from a multiple linear regression with first-order autocorrelation where the distribution of the error terms is not necessarily normal. The proposed permutation procedure can be directly conducted in the test without having to fit back to the model, which is not the same procedure as in previous permutation tests, and a proposed permutation test is considered based on a random permutation test. In addition, the asymptotic ...
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    A test statistic for selection of multivariate linear regression models 

    Srisuda Boonyim; Jirawan Jitthavech (National Institute of Development Administration, 2015)

    In this study, a test statistic used to select a multivariate linear regression model based on Mallows’s Cp with the same rationale as the SCp criterion from the system of equations Vichit Lorchirachoonkul and Jirawan Jitthavech (2012: 2386- 2394) proposed. The D statistic, which is the difference between the modified Cp statistics in the reduced model and in the full model, approximates to a standard normal distribution.
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    An alternative estimator for regression coefficients with outliers 

    Titirut Mekbunditkul; Pachitjanut Siripanich, advisor (National Institute of Development Administration, 2010)

    An important problem often found in a regression analysis is that a structural change in regression exists and/or the observed data contain outliers. These can lead to a violation of the Gauss-Markov assumptions and affect least squares (LS), rendering the regression inadequate. In this dissertation, an alternative regression model has been constructed from a combination of two principle ideas, namely the Tobit and piecewise regressions. This combined model, called the Tobit-piecewise (TP) regression model, can be suitably applied to cope with ...
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    Corrected score estimators in multivariate regression models with heteroscedastic measurement errors 

    Wannaporn Junthopas; Jirawan Jitthavech (National Institute of Development Administration, 2016)

    In this study, the knowledge of parameter estimation theory based on the corrected score (CS) approach is extended in a linear multivariate multiple regression model with heteroscedastic measurement errors (HME) and an unknown HME variance. The heteroscedasticity of the HME variance is assumed to be capable of being grouped into similar patterns where the sample of observations are assembled into several sub-samples with the property that the variances of the measurement error (ME) are homoscedastic within a group but heteroscedastic between ...
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    Estimation of regression coefficients with outliers 

    Pimpan Amphanthong; Prachoom Suwattee, advisor (National Institute of Development Administration, 2009)

    In linear models, the ordinary least squares estimators of have always turned out to be the best linear unbiased estimates. When the sample data contain outliers, the outliers may have a considerable effect on the least-squares estimates of , and an alternative approach to the problem is needed to obtain a better fit of the model or more precise estimates of . In this study, new weights were constructed for the sample data from two new influence functions and applied in the estimation of regression coefficients with outliers. Two sets of ...
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    Model selection criteria for autoregressive and autoregressive moving average models based on Kullback symmetric divergence 

    Rujirek Boosarawongse; Samruam Chongcharoen, chairperson (National Institute of Development Administration, 2004)
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    Nonparametric comparison of regression functions in two samples 

    Pimtong Srihera; Pachitjanut Siripanich, advisor (National Institute of Development Administration, 2004)
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    Outlier detection and parameter estimation in multivariate multiple regression (MMR) 

    Paweena Tangjuang; Pachitjanut Siripanich (National Institute of Development Administration, 2013)

    Outlier detection in Y-direction for multivariate multiple regression data is interesting since there are correlations between the dependent variables which is one cause of difficulty in detecting multivariate outliers, furthermore, the presence of the outliers may change the values of the estimators arbitrarily. Having an alternative method that can detect those outliers is necessary so that reliable results can be obtained. The multivariate outlier detection methods have been developed by many researchers. But in this study, Mahalanobis ...
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    Permutation test of partial regression coefficients 

    Siriwan Tantawanich; Pachitjanut Siripanich, advisor (National Institute of Development Administration, 2006)
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    Robustifying regression models 

    Renumas Gulasirima; Pachitjanut Siripanich, advisor (National Institute of Development Administration, 2006)
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    Selection of a system of simultaneous equations model 

    Warangkhana Keerativibool; Jirawan Jitthavech, advisor (National Institute of Development Administration, 2009)

    One of the most important problems in statistical modeling is to choose an appropriate model from a class of candidate models. In real life, we may not know what the true model is, but we hope to find a model that is a reasonably accurate representation. A model selection criterion represents a useful tool to judge the propriety of a fitted model, by assessing whether it offers an optimal balance between goodness of fit and parsimony, the attributes of the best model. In this dissertation, the Akaike information criterion for a system of simultaneous ...
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    Simple linear regression analysis for incomplete longitudinal data 

    Juthaphorn Saekhoo; Pachitjanut Siripanich, advisor (National Institute of Development Administration, 2008)