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Browsing K-State Electronic Theses, Dissertations, and Reports: 2004 - by Subject

Browsing K-State Electronic Theses, Dissertations, and Reports: 2004 - by Subject "Statistics (0463)"

Now showing items 1-20 of 105

A STUDY OF COVARIANCE STRUCTURE SELECTION FOR SPLIT-PLOT DESIGNS ANALYZED USING MIXED MODELS

Qiu, Chen (Kansas State University, 2014)

In the classic split-plot design where whole plots have a completely randomized design, the conventional analysis approach assumes a compound symmetry (CS) covariance structure for the errors of observation. However, often ...
TECHNOLOGY MEDIATED COMMUNICATION IN INTIMATE RELATIONSHIPS

Norton, Aaron (Kansas State University, 2014)

Very little research has been conducted to understand how the technology revolution has changed and impacted couple relationships. The proposed study examined the impact of technology on couples in committed relationships ...
ROBUST FITTING OF MIXTURE OF FACTOR ANALYZERS USING THE TRIMMED LIKELIHOOD ESTMATOR

YANG, LI (Kansas State University, 2014)

Mixtures of factor analyzers have been popularly used to cluster the high dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. ...
NONPARAMETRIC LACK-OF-FIT TESTS IN PRESENCE OF HETEROSCEDASTIC VARIANCES

Gharaibeh, Mohammed Mahmoud (Kansas State University, 2014)

It is essential to test the adequacy of a speciﬁed regression model in order to have cor- rect statistical inferences. In addition, ignoring the presence of heteroscedastic errors of regression models will lead to ...
IMPROVING THE PERFORMANCE OF THE PREDICTION ANALYSIS OF MICROARRAYS ALGORITHM VIA DIFFERENT THRESHOLDING METHODS AND HETEROSCEDASTIC MODELING

Sahtout, Mohammad Omar (Kansas State University, 2014)

This dissertation considers diﬀerent methods to improve the performance of the Prediction Analysis of Microarrays (PAM). PAM is a popular algorithm for high-dimensional classiﬁcation. However, it has a drawback of retaining ...
USING MARKOV CHAIN TO DESCRIBE THE PROGRESSION OF CHRONIC DISEASE

Davis, Sijia (Kansas State University, 2014)

A discrete-time Markov chain with stationary transition probabilities is often used for the purpose of investigating treatment programs and health care protocols for chronic disease. Suppose the patients of a certain chronic ...
Robust estimation of the number of components for mixtures of linear regression

Meng, Li (Kansas State University, 2014)

In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion ...
The impact of misspecification of nuisance parameters on test for homogeneity in zero-inflated Poisson model: a simulation study

Gao, Siyu (Kansas State University, 2014)

The zero-inflated Poisson (ZIP) model consists of a Poisson model and a degenerate distribution at zero. Under this model, zero counts are generated from two sources, representing a heterogeneity in the population. In ...
Statistical inference for varying coefficient models

Chen, Yixin (Kansas State University, 2014)

This dissertation contains two projects that are related to varying coefficient models. The traditional least squares based kernel estimates of the varying coefficient model will lose some efficiency when the error ...
A simulation comparison of two methods for controlling the experiment-wise Type I error rate of correlated tests for contrasts in one-way completely randomized designs

Jiao, Yuanfang (Kansas State University, 2014)

A Bonferroni and an ordered P-value solution to the problem of controlling the experiment-wise Type I error rate are studied and compared in terms of actual size and power when carrying out correlated tests. Although ...
Semiparametric mixture models

Xiang, Sijia (Kansas State University, 2014)

This dissertation consists of three parts that are related to semiparametric mixture models. In Part I, we construct the minimum profile Hellinger distance (MPHD) estimator for a class of semiparametric mixture models ...
Modeling proportions to assess the soil nematode community structure in a two year alfalfa crop

Zbylut, Joanna (Kansas State University, 2014)

The southern root-knot nematode (SRKN) and the weedy perennials, yellow nutsedge (YNS) and purple nutsedge (PNS) are simultaneously occurring pests in the irrigated agricultural soils of southern New Mexico. Previous ...
Statistical methods for diagnostic testing: an illustration using a new method for cancer detection

Sun, Xin (Kansas State University, 2013)

This report illustrates how to use two statistic methods to investigate the performance of a new technique to detect breast cancer and lung cancer at early stages. The two methods include logistic regression and classification ...
Robust mixture regression model fitting by Laplace distribution

Xing, Yanru (Kansas State University, 2013)

A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure ...
Individual mediating effects and the concept of terminal measures data

Serasinghe, Roshan Niranjala (Kansas State University, 2013)

Researches in the fields in science and statistics often go beyond the two-variable cause-and-effect relationship, and also try to understand what connects the causal relationship and what changes the magnitude or direction ...
Sparse and orthogonal singular value decomposition

Khatavkar, Rohan (Kansas State University, 2013)

The singular value decomposition (SVD) is a commonly used matrix factorization technique in statistics, and it is very e ective in revealing many low-dimensional structures in a noisy data matrix or a coe cient matrix ...
Prediction and variable selection in sparse ultrahigh dimensional additive models

Ramirez, Girly Manguba (Kansas State University, 2013)

The advance in technologies has enabled many fields to collect datasets where the number of covariates (p) tends to be much bigger than the number of observations (n), the so-called ultrahigh dimensionality. In this ...
Consistent bi-level variable selection via composite group bridge penalized regression

Seetharaman, Indu (Kansas State University, 2013)

We study the composite group bridge penalized regression methods for conducting bilevel variable selection in high dimensional linear regression models with a diverging number of predictors. The proposed method combines ...
Treatment heterogeneity and potential outcomes in linear mixed effects models

Richardson, Troy E. (Kansas State University, 2013)

Studies commonly focus on estimating a mean treatment effect in a population. However, in some applications the variability of treatment effects across individual units may help to characterize the overall effect of a ...
Parameter estimation of the Black-Scholes-Merton model

Teka, Kubrom Hisho (Kansas State University, 2013)

In financial mathematics, asset prices for European options are often modeled according to the Black-Scholes-Merton (BSM) model, a stochastic differential equation (SDE) depending on unknown parameters. A derivation of ...

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