Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Bayesian Analysis (1)
- Bayesian inference (1)
- Change point detection (1)
- Compound estimation (1)
- Derivative estimation (1)
-
- Design-based inference (1)
- Differential recruitment (1)
- Linear (1)
- Linear Regression (1)
- Misclassification on nodal attribute (1)
- National prevalence estimation (1)
- Nonparametric (1)
- Nonparametric regression (1)
- Regression (1)
- Respondent-driven sampling (1)
- Robust (1)
- Simple linear model (1)
- Statistics (1)
- Tuning parameter selection (1)
Articles 1 - 5 of 5
Full-Text Articles in Statistical Models
Bayesian Model For Detection Of Outliers In Linear Regression With Application To Longitudinal Data, Zahraa Al-Sharea
Bayesian Model For Detection Of Outliers In Linear Regression With Application To Longitudinal Data, Zahraa Al-Sharea
Graduate Theses and Dissertations
Outlier detection is one of the most important challenges with many present-day applications. Outliers can occur due to uncertainty in data generating mechanisms or due to an error in data recording/processing. Outliers can drastically change the study's results and make predictions less reliable. Detecting outliers in longitudinal studies is quite challenging because this kind of study is working with observations that change over time. Therefore, the same subject can produce an outlier at one point in time produce regular observations at all other time points. A Bayesian hierarchical modeling assigns parameters that can quantify whether each observation is an outlier …
Examination And Comparison Of The Performance Of Common Non-Parametric And Robust Regression Models, Gregory F. Malek
Examination And Comparison Of The Performance Of Common Non-Parametric And Robust Regression Models, Gregory F. Malek
Electronic Theses and Dissertations
ABSTRACT
Examination and Comparison of the Performance of Common Non-Parametric and Robust Regression Models
By
Gregory Frank Malek
Stephen F. Austin State University, Masters in Statistics Program,
Nacogdoches, Texas, U.S.A.
This work investigated common alternatives to the least-squares regression method in the presence of non-normally distributed errors. An initial literature review identified a variety of alternative methods, including Theil Regression, Wilcoxon Regression, Iteratively Re-Weighted Least Squares, Bounded-Influence Regression, and Bootstrapping methods. These methods were evaluated using a simple simulated example data set, as well as various real data sets, including math proficiency data, Belgian telephone call data, and faculty …
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
Arts & Sciences Electronic Theses and Dissertations
The general goal of this thesis is to investigate and examine some issues about post-selection inference which arises from the setting where statistical inference is carried out after a datadriven model selection step. In this setting, the classical inference theory which requires a fixed priori model becomes invalid since the selected model is a result of random event. Hence, a common practice in applied research which ignores the model selection and builds up confidence interval will result in misleading or even false conclusion. In this thesis, specifically, we first discusses some examples to show how the classical inference theory loses …
Inference From Network Data In Hard-To-Reach Populations, Isabelle Beaudry
Inference From Network Data In Hard-To-Reach Populations, Isabelle Beaudry
Doctoral Dissertations
The objective of this thesis is to develop methods to make inference about the prevalence of an outcome of interest in hard-to-reach populations. The proposed methods address issues specific to the survey strategies employed to access those populations. One of the common sampling methodology used in this context is respondent-driven sampling (RDS). Under RDS, the network connecting members of the target population is used to uncover the hidden members. Specialized techniques are then used to make inference from the data collected in this fashion. Our first objective is to correct traditional RDS prevalence estimators and their associated uncertainty estimators for …
Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu
Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu
Theses and Dissertations--Statistics
Firstly, we reviewed some popular nonparameteric regression methods during the past several decades. Then we extended the compound estimation (Charnigo and Srinivasan [2011]) to adapt random design points and heteroskedasticity and proposed a modified Cp criteria for tuning parameter selection. Moreover, we developed a DCp criteria for tuning paramter selection problem in general nonparametric derivative estimation. This extends GCp criteria in Charnigo, Hall and Srinivasan [2011] with random design points and heteroskedasticity. Next, we proposed a change point detection method via compound estimation for both fixed design and random design case, the adaptation of heteroskedasticity was considered for the method. …