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 229 for “"imputation"”.
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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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NBA Sleep Tracking Data Imputation
This thesis investigates imputation methods for nights of missing sleep wearable data from NBA Academy athletes. Sparsity in sleep tracking data arises as a result of behavioral non-compliance or device malfunction, hindering the NBA Academy's ability to provide actionable insights that improve …
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Imputation of Microsatellite Markers With Tag SNPs
… and genome wide SNP data. We investigate four imputation methods: Tagger, Vertex Discriminant Analysis (VDA), IMPUTE2, and BEAGLE. We achieve an accuracy of 93% with VDA in our subsample of Caucasians with manual 5-HTTLPR genotypes. Further, we find that for the entire Caucasian subsample …
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Modeling household residential choice using multiple imputation
… 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 data sets: …
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Evaluation of Imputation Methods Focusing on Categorical Outcomes
… from HIV positive subjects are incomplete. Imputation methods fill in educated guesses into the missing values in a dataset en abling the utilization of all collected information without discarding any observations. Several options are available and, in this work, the popular and freely …
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Probabilistic Models for Human Migration Forecasting and Residency Imputation
I develop probabilistic models to enhance the estimation and forecasting of human migration flows and residency. Using a Bayesian hierarchical approach, I first propose a model for forecasting global bilateral migration flows among the 200 most populous countries, producing well-calibrated …
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Missing values imputation and image registration for genetics applications
In this thesis, we address several common scenarios of corrupted data in data and image processing pipelines. The first is in the setting of clustered data with missing values. We design an algorithm for imputing missing values using optimal recovery and derive an error bound for non-negative …
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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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Large-scale inference and imputation for multi-tissue gene expression
… This dissertation develops inference and imputation methods for the analysis of gene expression data, an immensely rich and complex biomedical data modality, enabling integration across multiple tissues. The imputation task can strongly influence downstream applications, including …
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Leveraging genotypes imputation and polygenic risk scores in malaria susceptibility
… studies can be improved either via genotypes imputation approaches or by treating the whole genome of an individual as a risk predictor using Polygenic Risk Scores (PRS). However, imputation remains at modest in Africa populations with few (or no) studies (study) have evaluated the potential …
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Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering
… the true biological signals. As a result, imputation methods are used to estimate missing values and reduce the impact of dropouts on downstream analyses. Furthermore, the high-dimensionality of scRNA-seq data presents additional challenges in effectively partitioning cell populations. …
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The imputation of Christ's righteousness: A study of key Pauline texts
… through both Reformed and modern discussions of imputation. This chapter provides the backdrop that puts the exegetical chapters in perspective. Chapter 2 focuses on Romans 4: 1-8, with particular emphasis given to the Old Testament background of Genesis 15:6, the place of the text in Paul's …
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Genome-wide Genotype Imputation-Aspects of Quality, Performance and Practical Implementation
… required by different GWA tools. Genotype imputation which is a common technique in GWA, allows us to study the relationship of a phenotype at markers that are missing and even at completely un-typed markers. Moreover this technique helps us to infer both common and rare variants that are …
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Networked time series imputation via position-aware graph enhanced variational autoencoders
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Missing data imputation in a clinical registry with deep generative models
… with missing data include simple mean or zero imputation and multivariate imputation that needs a more complex modeling. With the explosion of data and the advancement in the machine learning techniques, more advanced deep generative models have shown the ability to learn complex distributions …
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Spatial Allocation, Imputation, and Sampling Methods for Timber Product Output Data
Data from the 2001 and 2003 timber product output (TPO) studies for Georgia were explored to determine new methods for handling missing data and finding suitable sampling estimators. Mean roundwood volume receipts per mill for the year 2003 were calculated using the methods developed by Rubin …
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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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Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling
… This research introduces a novel integrated imputation procedure that combines the imputation model selection with the cross-validation procedure for downstream model tuning without data "leakage"--the primary objective shifts from minimizing the imputation data error to minimizing the …
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Imputation of data missing due to truncation: Maximum likelihood estimates using symmetry property.
Imputation of data missing due to truncation: Maximum likelihood estimates using symmetry property.
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