Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 69 for “"multiple imputation"”.
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Modeling household residential choice using multiple imputation
… this thesis is to explore a possible technique - Multiple Imputation - to integrate observations from dissimilar data sets to meet the data requirements of random bidding models of the housing market, and to test the capability of such a model. The data used in this thesis come from two distinct …
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Missing data and multiple imputation: A sampling study with the SAGA cohort
… Using: Complete case analysis (CCA), Single imputation using predictive mean matching (SI-PMM), Multiple imputation using predictive mean matching (MI-PMM) and Multiple imputation using the default methods from MICE (MI-MICE) on the SAGA cohort, and fitting a Poisson model with robust error …
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Robustness of Multiple Imputation under Missing at Random (MAR) Mechanism: A Simulation Study
… emphasis is put on demonstrating how well multiple imputation works to deal with missing data under Missing at Random (MAR) mechanism with monotonic and non-monotonic missing data patterns for a range of percent missing under both normal and non-normal distributions. The results of this …
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The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models
… data methods on multilevel data (multilevel multiple imputation, multiple imputation ignoring the multilevel structure, and listwise deletion). The comparison of these methods was made under conditions known or believed to influence both the performance of missing data methods and multilevel …
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Deterioration Model for Ports in the Republic of Korea using Markov Chain Monte Carlo with Multiple Imputation
… MCMC (Markov Chain Monte Carlo), and MCMC with Multiple imputation, are finally proposed in this study. In addition, comparison between four models are carried out and good performance model is proposed.<br/><br/>This research provides deterioration model for port in South Korea, and more …
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Multiple Imputation of Missing Data in Structural Equation Models with Mediators and Moderators Using Gradient Boosted Machine Learning
… equation modeling with psychological data. Multiple imputation (MI) is one method used to estimate model parameters in the presence of missing data, while accounting for uncertainty due to the missing data. Unfortunately, commonly used MI methods are not equipped to handle categorical …
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Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders
… for missing data:: 1) case-wise deletion and: 2) multiple imputation. Results suggest that using several of the ASI, a tool already implemented in rehabilitation efforts, interventions can be tailored to address more closely all of the issues regarding the health and safety of substance abusing …
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Handling missing data in analyses of the UK women's cohort study
… in almost every variable. A number of simple imputation techniques, as well as multiple imputation developed by Rubin (1987), and multiple imputation by chained equations using the Gibbs sampling (Van Buuren, 1999), were explored in a number of illustrative analyses associated with the UKWCS. …
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The Single Imputation Technique in the Gaussian Mixture Model Framework
… proposed to deal with the missing data problem. Imputation is the most popular strategy for handling the missing data. Imputation for data analysis is the process to replace the missing values with any plausible values. Two most frequent imputation techniques cited in literature are the single …
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Imputing age at death for the deceased using household relationships
… the head of household are incorporated into the Multiple Imputation (MI) technique proposed by Rubin (1987) to impute the missing ages at death for the deceased.
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Methods for handling missing data in cohort studies where outcomes are truncated by death
… outcomes - inverse probability weighting, multiple imputation and linear increments - is made, focusing particularly on the setting where outcomes are missing due to both dropout and death. We show that when the dropout models are correctly specified for inverse probability weighting, and …
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Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
… with the missing data from among the numerous imputation methods that can be used. However, one might not know which imputation method is the best. The objective of this study is to evaluate the efficacy of five imputation methods. In Chapter Four, we have compared the performance of …
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Retail Price Time Series Imputation
… retail price time series datasets using data imputation methods. We introduce a new method called Retail Price Time Series Imputation (RPTSI). The basic RPTSI method uses an ensemble of three constituent methods for imputing retail prices in a univariate time series dataset based upon retail …
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Enhancing Primary Care Electronic Medical Record (EMR) Data in Alberta by Quality Assessment, Data Processing, and Linkage to Administrative Data
… surveillance. The third part explored multiple imputation and a pattern-matching algorithm for improving smoking status records in the EMR data. Lastly, EMR and administrative data for a cohort of hypertensive patients were linked and described. The CPCSSN process documentation and data …
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Selected topics in statistical discriminant analysis.
… dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction method to determine a low-dimensional hyperplane that preserves or nearly preserves the separation of the individual populations and the Bayes probability of …
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Cardiovascular disease risk prediction models: does one-score-fit-all?
… outline practical recommendations for applying multiple imputation in the external validation of risk prediction models in population-wide electronic health records; 3) To evaluate the prognostic performance and clinical utility of CVD risk prediction models in young adults with pre-existing …
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A GLMM analysis of data from the Sinovuyo Caring Families Program (SCFP)
… and observational video coding. Multiple imputation (using chained equations) procedures were used to impute missing information. Generalized linear Mixed Effect Models (GLMMs) were used to assess the impact of the intervention program on the responses, adjusted for possible …
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Statistical methods for outcome misclassification adjustment in causal inference and spatial classification.
… validation data. Regression calibration (RC) and multiple imputation (MI) are utilized to correct misclassified outcomes where a gold-standard device is not available. Spatial generalized linear mixed model (SGLMM) and indicator kriging (IK) are applied to spatial classification at unsampled …
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Analysis of Longitudinal Data With Missing Responses: A Study of Pain Control Cost
… cost for the remaining observations by applying multiple imputation. Multiple sets of complete imputed daily cost data are produced. A generalized estimating equations (GEE) model, is then applied to conduct analysis on each set of data, producing multiple analysis results. The correlation …
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Some Recent Advances in Non- and Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables
… utility of this model as a default engine for multiple imputation of mixed data in a large repeated-sampling study using data from the Survey of Income and Participation. I show that it improves substantially on its most popular competitor, multiple imputation by chained equations (MICE), while …
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