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 159 for “"data models"”.
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Semiparametric Bayesian Count Data Models
Count data models have a large number of pratical applications. However there can be several problems which prevent the use of the standard Poisson regression. We may detect individual unobserved heterogeneity, caused by missing covariates, and/or excess of zero observations in our data. Both …
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An experiment in collaboratively developed data models
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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On indexing large databases for advanced data models
In the last decade, the relational data model has been extended in numerous ways, including geographic information systems, abstract data types and object models, constraint and temporal databases, and on-line analytical processing. We study the indexing requirements of these data models. In many …
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Essays on Group Heterogeneity in Panel Data Models
… group heterogeneity has become popular in panel data models. Instead of modeling heterogeneity via unit-specific coefficients, the cross-sectional units are assumed to cluster into groups, and within each group, units share the same coefficients. This thesis develops an econometric framework to …
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Natural language image description: data, models, and evaluation
Made available in DSpace on 2016-03-02T19:33:52Z (GMT). No. of bitstreams: 4 HODOSH-DISSERTATION-2015.pdf: 33671796 bytes, checksum: 1f9350bc33a01a78722da502ecf52e0a (MD5) JAIR Permission.pdf: 184972 bytes, checksum: c985c2684d03383ef5292dbf559e1465 (MD5) LICENSE.txt: 4209 bytes, checksum: …
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Essays on Quantile Regression for Dynamic Panel Data Models
… quantile regression methods for dynamic panel data with fixed effects. We consider a penalized strategy designed to improve the properties of the dynamic panel data quantile regression instrumental variables estimator. The penalty involves l1 shrinkage of the fixed effects. We discuss a tuning …
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Architecture for data exchange among partially consistent data models
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.
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Nonparametric efficient estimation of prediction error for incomplete data models
Commonly accepted measures of prediction error, such as mean squared <br>error or R^2 typically fail to be identifiable with censored <br>observations. The Brier score is a loss function which is suitable for <br>the assessment of predictions made in terms of predicted probabilities <br>that are …
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Panel data models with nonadditive unobserved heterogeneity : estimation and inference
… and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest - means, variances, and other moments of the random coefficients - are estimated by cross sectional sample moments of GMM estimators applied separately to the …
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Designing end user information environments built on semistructured data models
… assigning properties not envisioned by the database administrator or the software engineer such as "good music to listen to when I am in a bad mood" or "excellent sushi place for taking foreign guests" is difficult in most programs because schemas are often cast in stone by a compiler or …
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Data, models and decisions for large-scale stochastic optimization problems
… the same time, firms have increasing access to data and models. Faced with such complex decisions and increasing access to data and models, how do we transform data and models into effective decisions? In this thesis, we address this question in the context of four important problems: the …
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Functional Data Models for Raman Spectral Data and Degradation Analysis
Functional data analysis (FDA) studies data in the form of measurements over a domain as whole entities. Our first focus is on the post-hoc analysis with pairwise and contrast comparisons of the popular functional ANOVA model comparing groups of functional data. Existing contrast tests assume …
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Three essays on nonlinear panel data models and quantile regression analysis
… fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force …
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Essays on Testing Hypotheses When Non-stationarity Exists in Panel Data Models
… of two essays on testing hypotheses in panel data models when non-stationarity exists in the model. This is done under the high-dimensional framework where both n (cross-section dimension) and T (time series dimension) are large. In the first essay, I discuss the limiting distribution of the …
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Semiparametric estimation methods for nonlinear panel data models and mismeasured dependent variables
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1996.
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Count Data Models for Injury Data from the National Health Interview Survey (NHIS)
… has been widely used in analyzing injury data from the National Health Interview Survey (NHIS). However, since its dependent variable is dichotomized to be either “1” (presence of an injury incident) or “0” (absence of an injury incident), logistic regression cannot provide …
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Three Essays on Testing for Cross-Sectional Dependence and Specification in Large Panel Data Models
… dependence and specification in large panel data models. The first two essays are based on the papers joint with Prof. Badi H. Baltagi and Prof. Chihwa Kao; the third essay is based on the working paper joint with Prof. Lee. The first essay considers testing for Sphericity with non-normality …
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Sample size determination for two sample binomial and Poisson data models based on Bayesian decision theory.
… to reach a desired expected power for binomial data under the Bayesian paradigm. We make improvements to their efforts that allow us to specify not only a desired expected Bayesian power, but also a more generic loss function and a desired expected Bayesian significance level, the latter having …
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