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An evaluation of mixed effects multilevel modeling under conditions of error term nonnormality /Shieh, Yannyann, January 1999 (has links)
Thesis (Ph. D.)University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 947960). Available also in a digital version from Dissertation Abstracts.

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Variable selection in the general linear model for censored dataYu, Lili. January 2007 (has links)
Thesis (Ph. D.)Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 121128).

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Orthogonal models for crossclassified observationsBust, Reg January 1987 (has links)
Includes bibliography. / This thesis describes methods of constructing models for crossclassified categorical data. In particular we discuss the construction of a class of approximating models and the selection of the most suitable model in the class. Examples of application are used to illustrate the methodology. The main purpose of the thesis is to demonstrate that it is both possible and advantageous to construct models which are specifically designed for the particular application under investigation. We believe that the methods described here allow the statistician to make good use of any expert knowledge which the client (typically a nonstatistician) might possess on the subject to which the data relate.

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Tests for linearity in time series: a comparative study.January 1986 (has links)
by Waisum Chan. / Thesis (M.Ph.)Chinese University of Hong Kong, 1986 / Includes bibliographical references.

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A new method of testing hypotheses in linear models.January 1996 (has links)
by TszKit Keung. / Thesis (M.Phil.)Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaf 81). / Chapter Chapter 1  Introduction  p.1 / Chapter Chapter 2  Testing Testable Hypotheses in Linear Models  p.8 / Chapter 2.1  A General Theory  p.9 / Chapter 2.2  The Method of Peixoto  p.17 / Chapter 2.3  The Method of Chan and Li  p.23 / Chapter Chapter 3  A New Method of Obtaining Equivalent Hypotheses  p.32 / Chapter Chapter 4  Constrained Linear Models  p.44 / Chapter 4.1  Hypothesis Testing in Constrained Linear Models  p.44 / Chapter 4.2  Linear Models with Missing Observations  p.50 / Chapter Chapter 5  Conclusions  p.71 / Appendix  p.74 / References  p.81

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Robust estimation for generalized additive models.January 2010 (has links)
Wong, Ka Wai. / Thesis (M.Phil.)Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 4649). / Abstracts in English and Chinese. / Chapter 1  Introduction  p.1 / Chapter 2  Background  p.4 / Chapter 2.1  Notation and Definitions  p.4 / Chapter 2.2  Influence Function of β  p.5 / Chapter 3  Methodology  p.7 / Chapter 3.1  Robust Estimating Equations  p.7 / Chapter 3.2  A General Algorithm for Robust GAM Estimation  p.9 / Chapter 4  Asymptotic Equivalence  p.12 / Chapter 5  Smoothing Parameter Selection  p.16 / Chapter 5.1  Robust CrossValidation  p.17 / Chapter 5.2  Robust Information Criteria  p.17 / Chapter 6  Multiple Covariates  p.19 / Chapter 7  Simulation Study  p.21 / Chapter 8  Real Data Examples  p.26 / Chapter 8.1  Air Pollution Data  p.26 / Chapter 8.2  Bronchitis Data  p.28 / Chapter 9  Concluding Remarks  p.31 / Chapter A  Auxiliary Lemmas and Proofs  p.32 / Chapter B  Fisher Consistency Correction  p.42 / Chapter B.1  Poisson distribution  p.42 / Chapter B.2  Bernoulli distribution  p.43 / Chapter C  Derivation of (5.2)  p.44 / Bibliography  p.46

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Applying higher order asymptotics to mixed linear modelsLyons, Benjamin 14 October 1997 (has links)
Mixed linear models are a time honored method of analyzing correlated data. However, there is still no method of calculating exact confidence intervals or pvalues for an arbitrary parameter in any mixed linear model. Instead, researchers must use either specialized approximate and exact tests that have been developed for particular models or rely on likelihood based approximate tests and confidence intervals which may be unreliable in problems with small sample sizes. This thesis develops procedures to improve small sample likelihood based inference in these important models. The first manuscript develops I.M. Skovgaard's modified directed likelihood for mixed linear models and shows how it is a general, accurate, and easy to apply method of improving inference in mixed linear models. In the second manuscript, O.E. BarndorffNielsen's approximate modified profile likelihood is applied to mixed linear models. This modified profile likelihood is a sensible generalization of the commonly used residual likelihood and can be applied if either a fixed or a covariance parameter is of interest. The final manuscript discusses how the design of a mixed linear model effects the accuracy of Skovgaard's modified likelihood and suggests a useful decomposition of that statistic. / Graduation date: 1998

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Diagnostics for the evaluation of spatial linear modelsThompson, Caryn M. (Caryn Marie) 06 June 1995 (has links)
Geostatistical linear interpolation procedures such as kriging require knowledge of the
covariance structure of the spatial process under investigation. In practice, the covariance of the
process is unknown, and must be estimated from the available data. As the quality of the
resulting predictions, and associated mean square prediction errors, depends on adequate
specification of the covariance structure, it is important that the analyst be able to detect
inadequacies in the specified covariance model. Casedeletion diagnostics are currently used by
geostatisticians to evaluate spatial models.
The second chapter of the thesis describes a particular casedeletion diagnostic based on
standardized PRESS residuals, and its use in assessing the predictive capacity of spatial
covariance models. Distributional properties of this statistic, denoted T [subscript PR], are discussed, and
a saddlepoint approximation to its distribution is derived. Guidelines for calculating
approximate pvalues for the statistic under an hypothesized covariance model are also given. A
simulation study demonstrates that the distributional and pvalue approximations are accurate.
The proposed method is illustrated through an example, and recommendations for calculation of
T [subscript PR], and associated approximate pvalues on a regional basis are given.
The third chapter investigates the behavior of the standardized PRESS residuals under
various misspecifications of the covariance matrix, V. A series of simulation studies show
consistent patterns in the standardized PRESS residuals under particular types of
misspecifications of V. It is observed that misspecification of V may lead to variability among
the standardized PRESS residuals greater or less than would be expected if V was correctly specified, depending on the nature of the misspecification. Based on this observation, an adjustment to normal probability plots of the standardized PRESS residuals is proposed. The adjusted normal probability plots may be used to identify potential improvements to covariance models, without requiring extensive further calculations. / Graduation date: 1996

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Generalized linear mixed models : development and comparison of different estimation methods /Nelson, Kerrie P. January 2002 (has links)
Thesis (Ph. D.)University of Washington, 2002. / Vita. Includes bibliographical references (p. 170182).

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Bayesian variable selection for GLMWang, Xinlei 28 August 2008 (has links)
Not available / text

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