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Filtering by: Advisor Lesser, Virginia Remove constraint Advisor: Lesser, Virginia Non-Academic Affiliation Oregon State University. Graduate School Remove constraint Non-Academic Affiliation: Oregon State University. Graduate School

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  • Information Criterion for Nonparametric Model-Assisted Survey Estimators

    Creator:
    James, Addison
    Abstract:
    Nonparametric model-assisted estimators have been proposed to improve estimates of finite population parameters. More efficient estimators are obtained when the parametric model is misspecified due to the flexibility of nonparametric models. In this dissertation, we derive information criteria to select appropriate auxiliary variables to use in an additive model-assisted method....
    Resource Type:
    Dissertation
    Full Text:
    Criterion for Nonparametric Model-Assisted Survey Estimators Abstract approved: Virginia M. Lesser Lan Xue

  • The design-based Empirical Orthogonal Functions model : an alternative for the analysis of spatio-temporal data

    Creator:
    Munoz-Hernandez, Breda
    Abstract:
    Random processes are monitored over space and time by a network of stations distributed across a spatial region. Researchers have developed several analysis techniques to model the random process. Some of them require assumptions on the spatio-temporal variability of the process. An alternative method to analyze spatio-temporal data that does...
    Resource Type:
    Dissertation
    Full Text:
    Data. Abstract approved: Virginia M. Lesser Random processes are monitored over space and time by

  • Neural network imputation : a new fashion or a good tool

    Creator:
    Amer, Safaa R.
    Abstract:
    Most statistical surveys and data collection studies encounter missing data. A common solution to this problem is to discard observations with missing data while reporting the percentage of missing observations in different output tables. Imputation is a tool used to fill in the missing values. This dissertation introduces the missing...
    Resource Type:
    Dissertation
    Full Text:
    dissertation possible. First and foremost, I would like to thank my advisor Dr. Virginia Lesser, who helped me

  • Propensity score methodology for nonignorable nonresponse

    Creator:
    Starcevich, Leigh Ann H.
    Abstract:
    When data are not missing at random, approaches to reduce nonresponse bias include subsampling nonresponding units and modeling. The objective of this thesis is to develop unbiased and precise model-assisted estimators of the population total that are applicable to data from a complex survey design with nonignorable nonresponse. When information...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: Virginia M. Lesser When data are not missing at random, approaches to

  • Modeling Density Dependence in the Presence of Measurement Error

    Creator:
    Payton, Quinn
    Abstract:
    Density dependence is an ecological concept concerning the mechanisms of change in the size of a population. The inability to census ecological populations confounds approaches to identify and quantify the level of density dependence. Statistical tests which ignore the presence of measurement error tend to result in misspeci fied type...
    Resource Type:
    Dissertation
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    Dependence in the Presence of Measurement Error Abstract approved: Paul A. Murtaugh Virginia M. Lesser

  • Propensity score adjustments using covariates in observational studies

    Creator:
    Yang, Daniel K.
    Abstract:
    In this thesis we develop a theoretical framework for the identification of situations where the equal frequency (EF) or equal variance (EV) subclassification may produce lower bias and/or variance of the estimator. We conduct simulation studies to examine the EF and EV approaches under different types of model misspecification. We...
    Resource Type:
    Dissertation
    Full Text:
    gratitude to my advisors, Dr. Virginia M. Lesser and Dr. Alix I. Gitelman for their patience, persistence