Now showing items 1-8 of 8

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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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    Estimating the Binomial proportion and the risk difference in multi-center studies with adjusting sparsity 

    Chukiat Viwatwongkasem; Jirawan Jitthavech, advisor (National Institute of Development Administration, 2005)
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    Estimation of parameters in a truncated gamma distribution with unknown truncation points 

    Prasert Ruannakarn; Anek Hirunraks, advisor (National Institute of Development Administration, 2003)
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    Estimators in finite population sampling on successive occasions 

    Bongkoj Wibultananun; Prachoom Suwattee, advisor (National Institute of Development Administration, 2006)
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    Model-assisted estimation in inverse sampling 

    Sureeporn Sungsuwan; Prachoom Suwattee, advisor (National Institute of Development Administration, 2010)

    Inverse sampling is a method of sampling which requires the continued drawing of units until certain specified conditions depending on the results of those draws have been fulfilled, and there are many studies on the estimation of inverse simple random sampling under a design-based approach. In this study, a modelassisted approach is used to estimate the parameters and the statistical properties of the estimators were evaluated under design-based inference. The main purpose of this dissertation is to propose estimators of the population total, ...
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    Probability density estimation using new kernel functions 

    Manachai Rodchuen; Prachoom Suwattee, advisor (National Institute of Development Administration, 2010)

    This dissertation propose two new kernel functions to estimate a density unction with small biases and errors .The errors of the estimats are measured using mean squared error (MSEf (f(x,X ))), mean integrated squared error (MISE(f) and asymptotic mean integrated squared error (AMISE(f) ), and The estimates of these error measures are also given .The AMISE0(f) of the density estimates are compared to the kernel density estimates for the uniform, Epanechnikov and Gaussian kernel functions. One kernel function is derived by minimizing the sum of ...
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    Three composite imputation methods for item nonresponse estimation in sample surveys 

    Watchareeporn Chaimongkol; Prachoom Suwattee, chairperson (National Institute of Development Administration, 2005)
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    Variance estimation for adaptive cluster sampling with a single primary unit and the partially systematic adaptive cluster sampling 

    Urairat Netharn; Dryver, Arthur L, advisor (National Institute of Development Administration, 2009)

    Two topics are investigated in this dissertation. The first concerns variance estimation when a single primary sampling unit is selected. Two new bias variance estimators, based on splitting the initial sample into sub-samples and regarding the initial sample as a stratified sample, are proposed. The results of this study indicated that both new variance estimators are underestimated. The first variance estimator is not preferable when the number of sub-samples is two because its relative bias is too large to be useful. Increasing the number of ...