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.
Results
Showing 1 to 20 of 381 for “"missing data"”.
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Sufficient Dimension Reduction with Missing Data
… (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) approach proposed by Li (1991). We show …
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Handling of Missing Data with Growth Mixture Models
… mixture models for inference with longitudinal data has introduced a wide range of research dedicated to testing the different aspects of the model. One area of research that has not drawn much attention, however, is the performance of growth mixture models with missing data and when using the …
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Modern methods for causal inference and missing data
The proliferation of data-driven approaches in a wide array of settings is one of the defining characteristic of the modern era. With this rise, there has been much focus on using data to answer causal questions, e.g. whether A causes a change in B. Furthermore aspects of data collection has given …
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Methods for Handling Missing Data for Multiple-Item Questionnaires
Missing data is a common problem, especially in the social and behavioral sciences. Modern missing data methods are underutilized in the industrial/organizational psychology and human resource management literature. Recommendations for handling missing data and default options in software packages …
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Addressing Deficiencies from Missing Data in Electronic Health Records
… health records (EHRs) contain a wealth of data that can be used to improve patient-centered outcomes. In particular, EHRs have been used for disease prediction, data-driven clinical decision support, patient trajectory modeling, etc. However, it is common that EHRs data contain substantial …
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Effects of missing data on zonal kinetic energy calculations,
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Meteorology, 1971.
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Approximate Bayesian approaches and semiparametric methods for handling missing data
… papers focusing on estimation and inference in missing data. In the first paper (Chapter 2), an approximate Bayesian approach is developed to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome variable. The …
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Topics in dimension reduction and missing data in statistical discrimination.
… linear discrimination procedures when monotone missing training data exists in the training data sets from two different multivariate normally distributed populations with unequal means but equal covariance matrices. We derive the maximum likelihood estimators (MLEs) for the partitioned …
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Processing of outliers and missing data in multivariate manufacturing data
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
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Handling missing data in analyses of the UK women's cohort study
Missing values are a problem in large-scale surveys with extensive questionnaires. The analysis of the complete records may yield inferences substantially different from those that would be obtained had no data been missing. The aim of this dissertation is to critically examine ways of handling …
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Improving applicability of the non-monotone unified estimate for missing data
In applied statistics missing data are a common problem. Performing a "complete case analysis" by removing individuals with missing data causes a loss of statistical power and can cause non-response bias. Inverse probability weighting is one method used to avoid non-response bias. However, when …
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UNDERSTANDING MISSING DATA IN REAL-TIME POLLUTION MONITORING SYSTEM IN CHINA
Using both remote sensing data on air pollution and publicly reported hourly PM2.5 data from ground-level monitoring stations, this paper examines whether the quality of the publicly reported PM2.5 is affected by selective reporting whereby high-level hourly pollution readings are dropped in the …
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A restriction method for the analysis of discrete longitudinal missing data.
… feel or function in their daily activities. Missing data is inevitable in most every clinical trial regardless of the amount of effort and pre-planning that originally went into a study. Many researchers often resort to ad hoc methods(e.g. case-deletion or mean imputation) when they are faced …
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Missing data handling in health sciences a neuro-fuzzy methods approach
Os levantamentos epidemiológicos de saúde periodontal exigem um tempo de exame extenso quando realizados através de avaliações completas da boca, o que sobrecarrega participantes e examinadores. Para aliviar isso, utiliza-se exames parciais da boca, omitindo intencionalmente alguns dados. No …
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Addressing Missing Data and Scalable Optimization for Data-driven Decision Making
Data-driven decision making has become a necessary commodity in virtually every domain of human endeavor, fueled by the exponential growth in the availability of data and the rapid increase in our computing power. In principle, if the collected data contain sufficient information, it is possible to …
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Missing data imputation in a clinical registry with deep generative models
Missing data is a common problem in all data driven algorithms. An incomplete dataset can bring bias to the trained model, or cause failures in the deployment of models that require a complete input. A clinical registry is a record of patients information about their health history, status and the …
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Missing data and multiple imputation: A sampling study with the SAGA cohort
Background: Missing data in epidemiological research is a common occurrence where there is no method that conclusively performs best. The aim of this thesis is to compare selected methods on the Stress-And Gene-Analysis (SAGA) cohort. Methods: Using: Complete case analysis (CCA), Single imputation …
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A Predictive Time-to-Event Modeling Approach with Longitudinal Measurements and Missing Data
… To handle the practical difficulty due to missing data in longitudinal measurements, and to accommodate observations at irregularly spaced time points, we propose a smoothed composite likelihood approach for estimations. The forward intensity function approach intrinsically incorporates the …
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Methods of Handling Missing Data in One Shot Response Based Power System Control
… of the existing one shot control subjected to missing PMU's data ranging from 0-10%. We can divide the thesis into two parts in which the first part includes understanding of the work done in [2] using another set of one-shot control combinations labelled as CC2 and the second part includes …
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Seven methods of handling missing data using samples from a national data base
The effectiveness of seven methods of handling missing data was investigated in a factorial design using random samples selected from the National Education Longitudinal Study of 1988 (NELS-88). Methods evaluated were listwise deletion, pairwise deletion, mean substitution, Buck's procedure, mean …
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