# Statistical Theory Commons™

## All Articles in Statistical Theory

1584 full-text articles. Page 1 of 37.

2017 Stephen F Austin State University

#### 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.

g_m_2002@live.com

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 ...

2017 Utah State University

#### Prediction Of Stress Increase In Unbonded Tendons Using Sparse Principal Component Analysis, Eric Mckinney

##### All Graduate Plan B and other Reports

While internal and external unbonded tendons are widely utilized in concrete structures, the analytic solution for the increase in unbonded tendon stress, Δ���, is challenging due to the lack of bond between strand and concrete. Moreover, most analysis methods do not provide high correlation due to the limited available test data. In this thesis, Principal Component Analysis (PCA), and Sparse Principal Component Analysis (SPCA) are employed on different sets of candidate variables, amongst the material and sectional properties from the database compiled by Maguire et al. [18]. Predictions of Δ��� are made via Principal Component Regression models, and the method ...

2017 Cleveland Clinic

#### Propensity Score Analysis With Matching Weights, Liang Li

##### Liang Li

The propensity score analysis is one of the most widely used methods for studying the causal treatment effect in observational studies. This paper studies treatment effect estimation with the method of matching weights. This method resembles propensity score matching but offers a number of new features including efficient estimation, rigorous variance calculation, simple asymptotics, statistical tests of balance, clearly identified target population with optimal sampling property, and no need for choosing matching algorithm and caliper size. In addition, we propose the mirror histogram as a useful tool for graphically displaying balance. The method also shares some features of the inverse ...

2017 Chapman University

#### Gilmore Girls And Instagram: A Statistical Look At The Popularity Of The Television Show Through The Lens Of An Instagram Page, Brittany Simmons

##### Student Research Day Abstracts and Posters

After going on the Warner Brothers Tour in December of 2015, I created a Gilmore Girls Instagram account. This account, which started off as a way for me to create edits of the show and post my photos from the tour turned into something bigger than I ever could have imagined. In just over a year I have over 55,000 followers. I post content including revival news, merchandise, and edits of the show that have been featured in Entertainment Weekly, Bustle, E! News, People Magazine, Yahoo News, & GilmoreNews.

I created a dataset of qualitative and quantitative outcomes from my ...

Random Regression Models Based On The Elliptically Contoured Distribution Assumptions With Applications To Longitudinal Data, 2017 University of Alabama at Birmingham

#### Random Regression Models Based On The Elliptically Contoured Distribution Assumptions With Applications To Longitudinal Data, Alfred A. Bartolucci, Shimin Zheng, Sejong Bae, Karan P. Singh

##### Shimin Zheng

We generalize Lyles et al.’s (2000) random regression models for longitudinal data, accounting for both undetectable values and informative drop-outs in the distribution assumptions. Our models are constructed on the generalized multivariate theory which is based on the Elliptically Contoured Distribution (ECD). The estimation of the fixed parameters in the random regression models are invariant under the normal or the ECD assumptions. For the Human Immunodeficiency Virus Epidemiology Research Study data, ECD models fit the data better than classical normal models according to the Akaike (1974) Information Criterion. We also note that both univariate distributions of the random intercept ...

A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, 2017 Amrita Vishwa Vidyapeetham, Amrita University

#### A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, Joghee Ravichandran

##### Journal of Modern Applied Statistical Methods

In most empirical studies (clinical, network modeling, and survey-based and aeronautical studies, etc.), sample observations are drawn from population to analyze and draw inferences about the population. Such analysis is done with reference to a measurable quality characteristic of a product or process of interest. However, fixing a sample size is an important task that has to be decided by the experimenter. One of the means in deciding an appropriate sample size is the fixation of error limit and the associated confidence level. This implies that the analysis based on the sample used must guarantee the prefixed error and confidence ...

Multiple Ratio Imputation By The Emb Algorithm: Theory And Simulation, 2017 Tokyo University of Foreign Studies

#### Multiple Ratio Imputation By The Emb Algorithm: Theory And Simulation, Masayoshi Takahashi

##### Journal of Modern Applied Statistical Methods

Although multiple imputation is the gold standard of treating missing data, single ratio imputation is often used in practice. Based on Monte Carlo simulation, the Expectation-Maximization with Bootstrapping (EMB) algorithm to create multiple ratio imputation is used to fill in the gap between theory and practice.

Selection Of Statistical Software For Data Scientists And Teachers, 2017 Valparaiso University

#### Selection Of Statistical Software For Data Scientists And Teachers, Ceyhun Ozgur, Min Dou, Yang Li, Grace Rogers

##### Journal of Modern Applied Statistical Methods

The need for analysts with expertise in big data software is becoming more apparent in today’s society. Unfortunately, the demand for these analysts far exceeds the number available. A potential way to combat this shortage is to identify the software sought by employers and to align this with the software taught by universities. This paper will examine multiple data analysis software – Excel add-ins, SPSS, SAS, Minitab, and R – and it will outline the cost, training, statistical methods/tests/uses, and specific uses within industry for each of these software. It will further explain implications for universities and students.

2017 University of Buffalo

#### Book Review: Multivariate Statistical Methods, A Primer, C. R. Rao

##### Journal of Modern Applied Statistical Methods

Multivariate Statistical Methods, A Primer, 4th Ed. Bryan F. J. Manly and Jorge A. Navarro Alberto. NY: Chapman & Hall / CRC Press. 2016. 264 p. ISBN 10: 1498728960 / ISBN 13: 978-1498728966

Errors In A Program For Approximating Confidence Intervals, 2017 University of California Los Angeles

#### Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane

##### Journal of Modern Applied Statistical Methods

An SPSS script previously presented in this journal contained nontrivial flaws. The script should not be used as written. A call is renewed for validation of new software.

2017 Northern Illinois University

#### In Response To Frane, "Errors In A Program For Approximating Confidence Intervals", David A. Walker

##### Journal of Modern Applied Statistical Methods

A rebuttal to Frane's letter to the Editor in this issue.

Vol. 16, No. 1 (Full Issue), 2017 Wayne State University

#### Vol. 16, No. 1 (Full Issue), Jmasm Editors

##### Journal of Modern Applied Statistical Methods

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Monte Carlo Study Of Some Classification-Based Ridge Parameter Estimators, 2017 Ladoke Akintola University of Technology

#### Monte Carlo Study Of Some Classification-Based Ridge Parameter Estimators, Adewale Folaranmi Lukman, Kayode Ayinde, Adegoke S. Ajiboye

##### Journal of Modern Applied Statistical Methods

Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been proposed. In this study, estimators based on Dorugade (2014) and Adnan et al. (2014) were classified into different forms and various types using the idea of Lukman and Ayinde (2015). Some new ridge estimators were proposed. Results shows that the proposed estimators based on Adnan et al. (2014) perform generally better than the existing ones.

The Double Prior Selection For The Parameter Of Exponential Life Time Model Under Type Ii Censoring, 2017 Som-Lalit College of Commerce, Ahmedabad

#### The Double Prior Selection For The Parameter Of Exponential Life Time Model Under Type Ii Censoring, Ronak M. Patel, Achyut C. Patel

##### Journal of Modern Applied Statistical Methods

A comparison of double informative priors assumed for the parameter of exponential life time model is considered. Three different sets of double priors are included, and the results are compared with a forth single prior. The data is Type II censored and Bayes estimators for the parameter and reliability are carried out under a squared error loss function in the cases of the four different sets of prior distributions. The predictive distribution was derived for future failure time and also for the remaining ordered failure times after the first r failure times have been observed. Corresponding Bayes credible equal tail ...

A Comparison Of Depth Functions In Maximal Depth Classification Rules, 2017 Federal University of Technology, Akure, Nigeria

#### A Comparison Of Depth Functions In Maximal Depth Classification Rules, Olusola Samuel Makinde, Adeyinka Damilare Adewumi

##### Journal of Modern Applied Statistical Methods

Data depth has been described as alternative to some parametric approaches in analyzing many multivariate data. Many depth functions have emerged over two decades and studied in literature. In this study, a nonparametric approach to classification based on notions of different data depth functions is considered and some properties of these methods are studied. The performance of different depth functions in maximal depth classifiers is investigated using simulation and real data with application to agricultural industry.

Confidence Intervals For The Scaled Half-Logistic Distribution Under Progressive Type-Ii Censoring, 2017 Department of Statistics, Ajara Mahavidyalaya, Ajara

#### Confidence Intervals For The Scaled Half-Logistic Distribution Under Progressive Type-Ii Censoring, Kiran Ganpati Potdar, D. T. Shirke

##### Journal of Modern Applied Statistical Methods

Confidence interval construction for the scale parameter of the half-logistic distribution is considered using four different methods. The first two are based on the asymptotic distribution of the maximum likelihood estimator (MLE) and log-transformed MLE. The last two are based on pivotal quantity and generalized pivotal quantity, respectively. The MLE for the scale parameter is obtained using the expectation-maximization (EM) algorithm. Performances are compared with the confidence intervals proposed by Balakrishnan and Asgharzadeh via coverage probabilities, length, and coverage-to-length ratio. Simulation results support the efficacy of the proposed approach.

A New Estimator For The Pickands Dependence Function, 2017 Centre of Mathematics of the University of Minho, Braga, Portugal

#### A New Estimator For The Pickands Dependence Function, Marta Ferreira

##### Journal of Modern Applied Statistical Methods

The Pickands dependence function characterizes an extreme value copula, a useful tool in the modeling of multivariate extremes. A new estimator is presented along with its convergence properties and performance through simulation.

A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, 2017 Hacettepe University

#### A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, Nilgün Özgül, Hülya Çıngı

##### Journal of Modern Applied Statistical Methods

An exponential-type estimator is developed for the population mean of the sensitive study variable based on various Randomized Response Techniques (RRT) using a non-sensitive auxiliary variable. The mean squared error (MSE) of the proposed estimator is derived for generalized RRT models. The proposed estimator is compared with competitors in a simulation study and an application. The proposed estimator is found to be more efficient using a non-sensitive auxiliary variable.

Control Charts For Mean For Non-Normally Correlated Data, 2017 Vikram University, Ujjain, India

#### Control Charts For Mean For Non-Normally Correlated Data, J. R. Singh, Ab Latif Dar

##### Journal of Modern Applied Statistical Methods

Traditionally, quality control methodology is based on the assumption that serially-generated data are independent and normally distributed. On the basis of these assumptions the operating characteristic (OC) function of the control chart is derived after setting the control limits. But in practice, many of the basic industrial variables do not satisfy both the assumptions and hence one may doubt the validity of the inferences drawn from the control charts. In this paper the power of the control chart for the mean is examined when both the assumptions of independence and normality are not tenable. The OC function is calculated and ...

Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, 2017 K.L.N. College of Engineering, Tamil Nadu, India

#### Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, B. Vijaya Lakshmi, V. Mohan

##### Journal of Modern Applied Statistical Methods

Shape-based feature extraction in content-based image retrieval is an important research area at present. An algorithm is presented, based on shape features, to enhance the set of features useful in a leaf identification system.