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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 21 to 24 of 24 (0.255 seconds)| 21 |
Multivariate Skew-t Distributions in Econometrics and EnvironmetricsMarchenko, Yulia V. 2010 December 1900 (has links)
This dissertation is composed of three articles describing novel approaches for
analysis and modeling using multivariate skew-normal and skew-t distributions in
econometrics and environmetrics.
In the first article we introduce the Heckman selection-t model. Sample selection
arises often as a result of the partial observability of the outcome of interest in
a study. In the presence of sample selection, the observed data do not represent a
random sample from the population, even after controlling for explanatory variables.
Heckman introduced a sample-selection model to analyze such data and proposed a
full maximum likelihood estimation method under the assumption of normality. The
method was criticized in the literature because of its sensitivity to the normality assumption.
In practice, data, such as income or expenditure data, often violate the
normality assumption because of heavier tails. We first establish a new link between
sample-selection models and recently studied families of extended skew-elliptical distributions.
This then allows us to introduce a selection-t model, which models the
error distribution using a Student’s t distribution. We study its properties and investigate
the finite-sample performance of the maximum likelihood estimators for
this model. We compare the performance of the selection-t model to the Heckman
selection model and apply it to analyze ambulatory expenditures.
In the second article we introduce a family of multivariate log-skew-elliptical distributions,
extending the list of multivariate distributions with positive support. We
investigate their probabilistic properties such as stochastic representations, marginal
and conditional distributions, and existence of moments, as well as inferential properties.
We demonstrate, for example, that as for the log-t distribution, the positive
moments of the log-skew-t distribution do not exist. Our emphasis is on two special
cases, the log-skew-normal and log-skew-t distributions, which we use to analyze U.S.
precipitation data.
Many commonly used statistical methods assume that data are normally distributed.
This assumption is often violated in practice which prompted the development
of more flexible distributions. In the third article we describe two such multivariate
distributions, the skew-normal and the skew-t, and present commands for
fitting univariate and multivariate skew-normal and skew-t regressions in the statistical
software package Stata.
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Advances and Applications of Experimental Measures to Test Behavioral Saving Theories and a Method to Increase Efficiency in Binary and Multiple Treatment AssignmentSchneider, Sebastian Olivier 24 November 2017 (has links)
No description available.
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Analyzing Crime Dynamics and Investigating the Great American Crime DeclineShaik, Salma 15 September 2022 (has links)
No description available.
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GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS DataDalumpines, Ron 26 September 2014 (has links)
Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use).
The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit.
There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. / Dissertation / Doctor of Philosophy (PhD)
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