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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 94

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 ...
Robust mixture regression modeling with Pearson type VII distribution

Zhang, Jingyi (Kansas State University, 2013)

A robust estimation procedure for parametric regression models is proposed in the paper by assuming the error terms follow a Pearson type VII distribution. The estimation procedure is implemented by an EM algorithm based ...
Statistical identification of metabolic reactions catalyzed by gene products of unknown function

Zheng, Lianqing (Kansas State University, 2013)

High-throughput metabolite analysis is an approach used by biologists seeking to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the ...
Modeling and computations of multivariate datasets in space and time

Demel, Samuel Seth (Kansas State University, 2013)

Spatio-temporal and/or multivariate dependence naturally occur in datasets obtained in various disciplines; such as atmospheric sciences, meteorology, engineering and agriculture. There is a great deal of need to effectively ...
Assessing regional volatility and estimating regional cotton acres in the United States

Holmes, Beth (Kansas State University, 2013)

The objective of the research is to understand the volatility of cotton acres and estimate planted acres based on the factors that drive volatility in the United States at a regional level. Estimating cotton acres is ...
A case study in applying generalized linear mixed models to proportion data from poultry feeding experiments

Shannon, Carlie (Kansas State University, 2013)

This case study was motivated by the need for effective statistical analysis for a series of poultry feeding experiments conducted in 2006 by Kansas State University researchers in the department of Animal Science. Some ...
Semi-parametric estimation in Tobit regression models

Chen, Chunxia (Kansas State University, 2013)

In the classical Tobit regression model, the regression error term is often assumed to have a zero mean normal distribution with unknown variance, and the regression function is assumed to be linear. If the normality ...
Robust mixture linear EIV regression models by t-distribution

Liu, Yantong (Kansas State University, 2013)

A robust estimation procedure for mixture errors-in-variables linear regression models is proposed in the report by assuming the error terms follow a t-distribution. The estimation procedure is implemented by an EM algorithm ...
Time series and spatial analysis of crop yield

Assefa, Yared (Kansas State University, 2013)

Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have ...
Robust linear regression

Bai, Xue (Kansas State University, 2012)

In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value ...
The performance and robustness of confidence intervals for the median of a symmetric distribution constructed assuming sampling from a Cauchy distribution

Cao, Jennifer Yue (Kansas State University, 2012)

Trimmed means are robust estimators of location for distributions having heavy tails. Theory and simulation indicate that little efficiency is lost under normality when using appropriately trimmed means and that their use ...
A simulation study of the robustness of prediction intervals for an independent observation obtained from a random sample from an assumed location-scale family of distributions

Makarova, Natalya (Kansas State University, 2012)

Suppose that based on data consisting of independent repetitions of an experiment a researcher wants to predict the outcome of the next independent outcome of the experiment. The researcher models the data as being ...

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