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Articles 961 - 990 of 1395

Full-Text Articles in Applied Statistics

Comparative Power Of The Independent T, Permutation T, And Wilcoxontests, Michèle Weber, Shlomo Sawilowsky May 2009

Comparative Power Of The Independent T, Permutation T, And Wilcoxontests, Michèle Weber, Shlomo Sawilowsky

Journal of Modern Applied Statistical Methods

The nonparametric Wilcoxon Rank Sum (also known as the Mann-Whitney U) and the permutation t-tests are robust with respect to Type I error for departures from population normality, and both are powerful alternatives to the independent samples Student’s t-test for detecting shift in location. The question remains regarding their comparative statistical power for small samples, particularly for non-normal distributions. Monte Carlo simulations indicated the rank-based Wilcoxon test was found to be more powerful than both the t and the permutation t-tests.

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Industrialization In Animal Agriculture: A Kalman Filter Analysis, Oya S. Erdogdu, Levent Ozbek May 2009

Industrialization In Animal Agriculture: A Kalman Filter Analysis, Oya S. Erdogdu, Levent Ozbek

Journal of Modern Applied Statistical Methods

Studies discussing the effects of technological developments on (animal) agricultural production argue that the effective usage of chemicals and genetic engineering increase control over production processes, which in turn decreases seasonality (one significant factor defining agricultural production) significantly and brings standardization to production. Studies on broilery also show that production is not limited by nature determined seasons. Supply side changes accompanied by changes in demand have led to more healthier, standardized products. Using tools of economics and statistics, this study documents this transformation in animal agricultural production of beef, pork and milk. Results indicate decreasing seasonality, thus the industralization of …

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Aligned Rank Tests For Interactions In Split-Plot Designs: Distributional Assumptions And Stochastic Heterogeneity, T. Mark Beasley, Bruno D. Zumbo May 2009

Aligned Rank Tests For Interactions In Split-Plot Designs: Distributional Assumptions And Stochastic Heterogeneity, T. Mark Beasley, Bruno D. Zumbo

Journal of Modern Applied Statistical Methods

Three aligned rank methods for transforming data from multiple group repeated measures (split-plot) designs are reviewed. Univariate and multivariate statistics for testing the interaction in split-plot designs are elaborated. Computational examples are presented to provide a context for performing these ranking procedures and statistical tests. SAS/IML and SPSS syntax code to perform the procedures is included in the Appendix.

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The Comparison Of Model Selection Criteria When Selecting Among Competing Hierarchical Linear Models, Tiffany A. Whittaker, Carolyn F. Furlow May 2009

The Comparison Of Model Selection Criteria When Selecting Among Competing Hierarchical Linear Models, Tiffany A. Whittaker, Carolyn F. Furlow

Journal of Modern Applied Statistical Methods

Little is known about the use and accuracy of model selection criteria when selecting among a set of competing multilevel models. The practices of applied researchers and the performance of five model selection criteria are examined when selecting the correct multilevel model using simulation techniques.

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Bias In Stabilized Sieve Sampling, Liming Guan, John P. Wendell May 2009

Bias In Stabilized Sieve Sampling, Liming Guan, John P. Wendell

Journal of Modern Applied Statistical Methods

The stabilized sieve sample selection method (SSM) is considered to be a probability proportional to size (PPS) sampling method with an unbiased estimator (Horgan 1997, 1998). This article demonstrates that SSM does not select items with PPS and that the point estimator is biased.

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A New Approximate Bayesian Approach For Decision Making About The Variance Of A Gaussian Distribution Versus The Classical Approach, Vincent A. R. Camara May 2009

A New Approximate Bayesian Approach For Decision Making About The Variance Of A Gaussian Distribution Versus The Classical Approach, Vincent A. R. Camara

Journal of Modern Applied Statistical Methods

Rules of decision-making about the variance of a Gaussian distribution are obtained and compared. Considering the square error loss function, an approximate Bayesian decision rule for the variance of a normal population is derived. Using normal data and SAS software, the obtained approximate Bayesian test results were compared to their counterparts obtained with the well-known classical decision rule. It is shown that the proposed approximate Bayesian decision rule relies only on observations. The classical decision rule, which uses the Chi-square statistic, does not always yield the best results: the proposed approach often performs better.

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Which Is The Best Parametric Statistical Method For Analyzing Delphi Data?, Hiral A. Shah, Sema A. Kalaian May 2009

Which Is The Best Parametric Statistical Method For Analyzing Delphi Data?, Hiral A. Shah, Sema A. Kalaian

Journal of Modern Applied Statistical Methods

This study compares the three parametric statistical methods: coefficient of variation, Pearson correlation coefficient, and F-test to obtain reliability in a Delphi study that involved more than 100 participants. The results of this study indicated that coefficient of variation was the best procedure to obtain reliability in such a study.

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A Socratic Dialogue, Vance W. Berger May 2009

A Socratic Dialogue, Vance W. Berger

Journal of Modern Applied Statistical Methods

Socrates has found some aspects of medical biostatistics a bit confusing, and wishes to discuss some of these issues with Simplicio, a prominent medical researcher. This Socratic dialogue will shed some light on the errant use of parametric analyses in clinical trials.

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A Comparison Of Maximum Likelihood And Expected A Posteriori Estimation For Polychoric Correlation Using Monte Carlo Simulation, Jinsong Chen, Jaehwa Choi May 2009

A Comparison Of Maximum Likelihood And Expected A Posteriori Estimation For Polychoric Correlation Using Monte Carlo Simulation, Jinsong Chen, Jaehwa Choi

Journal of Modern Applied Statistical Methods

This study aims to compare the maximum likelihood (ML) and expected a posterior (EAP) estimation for polychoric correlation (PCC) under diverse conditions, especially when considering a sample size. As the ML is the classical solution to estimate PCC, the EAP is a new method based on Bayes’ theorem. Different types of prior distributions are also adapted to investigate the sensitivity of prior distribution onto the PCC estimate for the EAP case. The Monte Carlo simulation is used for this comparison by a specialized program code in MATLAB.

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Why Study Applied/Agricultural Economics, Matt Bogard Jan 2009

Why Study Applied/Agricultural Economics, Matt Bogard

Agriculture Department Seminar Series

Agricultural Economics is a very applied field covering many topics beyond those stereotypically thought of as pertaining to agriculture. These may include finance and risk management, environmental and natural resource economics, game theory, or public policy analysis to name a few.

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The Deterrent Effect Of Death Penalty Eligibility: Evidence From The Adoption Of Child Murder Eligibility Factors, Michael D. Frakes, Matthew Harding Jan 2009

The Deterrent Effect Of Death Penalty Eligibility: Evidence From The Adoption Of Child Murder Eligibility Factors, Michael D. Frakes, Matthew Harding

Faculty Scholarship

We draw on within-state variations in the reach of capital punishment statutes between 1977 and 2004 to identify the deterrent effects associated with capital eligibility. Focusing on the most prevalent eligibility expansion, we estimate that the adoption of a child murder factor is associated with an approximately 20% reduction in the homicide rate of youth victims. Eligibility expansions may enhance deterrence by (1) paving the way for more executions and (2) providing prosecutors with greater leverage to secure enhanced non-capital sentences. While executions themselves are rare, this latter channel is likely to be triggered fairly regularly, providing a reasonable basis …

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"G" And "H" Control Charts And Risk Analysis In The Banking Industry, James L. Bossert Dec 2008

"G" And "H" Control Charts And Risk Analysis In The Banking Industry, James L. Bossert

All-Inclusive List of Electronic Theses and Dissertations

This research investigates the utilization of a new control chart in the Banking industry to minimize financial risk. There are processes in the banking industry that do not lend themselves to traditional control chart applications. These processes tend to be high volume. High volume meaning over a million transactions a day and a requirement for high levels of accuracy characterize these processes. The research has looked at these processes and applied the "g" control charts to determine if they provide timely feedback to the banking industry. The value of this research will give the banking industry an opportunity to react …

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Multi-Group Confirmatory Factor Analysis For Testing Measurement Invariance In Mixed Item Format Data, Kim H. Koh, Bruno D. Zumbo Nov 2008

Multi-Group Confirmatory Factor Analysis For Testing Measurement Invariance In Mixed Item Format Data, Kim H. Koh, Bruno D. Zumbo

Journal of Modern Applied Statistical Methods

This simulation study investigated the empirical Type I error rates of using the maximum likelihood estimation method and Pearson covariance matrix for multi-group confirmatory factor analysis (MGCFA) of full and strong measurement invariance hypotheses with mixed item format data that are ordinal in nature. The results indicate that mixed item formats and sample size combinations do not result in inflated empirical Type I error rates for rejecting the true measurement invariance hypotheses. Therefore, although the common methods are in a sense sub-optimal, they don’t lead to researchers claiming that measures are functioning differently across groups – i.e., a lack of …

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Estimating Explanatory Power In A Simple Regression Model Via Smoothers, Rand R. Wilcox Nov 2008

Estimating Explanatory Power In A Simple Regression Model Via Smoothers, Rand R. Wilcox

Journal of Modern Applied Statistical Methods

Consider the regression model Y = γ(X) + ε , where γ(X) is some conditional measure of location associated with Y , given X. Let Υ̂ be some estimate of Y, given X, and let τ2 (Y) be some measure of variation. Explanatory power is η2 = τ2 (Υ̂) /τ2(Y) . When γ(X) = β0 + β1X and τ2(Y) is the variance of Y , η2 = ρ2 , …

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Data Mining Ceo Compensation, Susan M. Adams, Atul Gupta, Dominique M. Haughton, John D. Leeth Nov 2008

Data Mining Ceo Compensation, Susan M. Adams, Atul Gupta, Dominique M. Haughton, John D. Leeth

Journal of Modern Applied Statistical Methods

The need to pre-specify expected interactions between variables is an issue in multiple regression. Theoretical and practical considerations make it impossible to pre-specify all possible interactions. The functional form of the dependent variable on the predictors is unknown in many cases. Two ways are described in which the data mining technique Multivariate Adaptive Regression Splines (MARS) can be utilized: first, to obtain possible improvements in model specification, and second, to test for the robustness of findings from a regression analysis. An empirical illustration is provided to show how MARS can be used for both purposes.

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Least Squares Percentage Regression, Chris Tofallis Nov 2008

Least Squares Percentage Regression, Chris Tofallis

Journal of Modern Applied Statistical Methods

In prediction, the percentage error is often felt to be more meaningful than the absolute error. We therefore extend the method of least squares to deal with percentage errors, for both simple and multiple regression. Exact expressions are derived for the coefficients, and we show how such models can be estimated using standard software. When the relative error is normally distributed, least squares percentage regression is shown to provide maximum likelihood estimates. The multiplicative error model is linked to least squares percentage regression in the same way that the standard additive error model is linked to ordinary least squares regression.

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Application Of Dynamic Poisson Models To Japanese Cancer Mortality Data, Shuichi Midorikawa, Etsuo Miyaoka, Bruce Smith Nov 2008

Application Of Dynamic Poisson Models To Japanese Cancer Mortality Data, Shuichi Midorikawa, Etsuo Miyaoka, Bruce Smith

Journal of Modern Applied Statistical Methods

A dynamic Poisson model is used with a Bayesian approach to modeling to predict cancer mortality. The complexity of the posterior distribution prohibits direct evaluation of the posterior, and so parameters are estimated by using a Markov Chain Monte Carlo method. The model is applied to analyze lung and stomach cancer data which have been collected in Japan.

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A Randomization Method To Control The Type I Error Rates In Best Subset Regression, Yasser A. Shehata, Paul White Nov 2008

A Randomization Method To Control The Type I Error Rates In Best Subset Regression, Yasser A. Shehata, Paul White

Journal of Modern Applied Statistical Methods

A randomization method for the assessment of statistical significance for best subsets regression is given. The procedure takes into account the number of potential predictors and the inter-dependence between predictors. The approach corrects a non-trivial problem with Type I errors and can be used to assess individual variable significance.

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Comparing Factor Loadings In Exploratory Factor Analysis: A New Randomization Test, W. Holmes Finch, Brian F. French Nov 2008

Comparing Factor Loadings In Exploratory Factor Analysis: A New Randomization Test, W. Holmes Finch, Brian F. French

Journal of Modern Applied Statistical Methods

Factorial invariance testing requires a referent loading to be constrained equal across groups. This study introduces a randomization test for comparing group exploratory factor analysis loadings so as to identify an invariant referent. Results show that it maintains the Type I error rate while providing adequate power under most conditions.

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Variance Estimation In Time Series Regression Models, Samir Safi Nov 2008

Variance Estimation In Time Series Regression Models, Samir Safi

Journal of Modern Applied Statistical Methods

The effect of variance estimation of regression coefficients when disturbances are serially correlated in time series regression models is studied. Variance estimation enters into confidence interval estimation, hypotheses testing, spectrum estimation, and expressions for the estimated standard error of prediction. Using computer simulations, the robustness of various estimators, including Estimated Generalized Least Squares (EGLS) was considered. The estimates of variance of the coefficient estimators produced by computer packages were considered. Models were generated with a second order auto-correlated error structure, considering the robustness of estimators based upon misspecified order. Ordinary Least Squares (OLS) (order zero) estimates outperformed first order EGLS. …

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Two Dimension Marginal Distributions Of Crossing Time And Renewal Numbers Related To Two-Stage Erlang Processes, Mir Ghulam Hyder Talpur, Iffat Zamir, M. Masoom Ali Nov 2008

Two Dimension Marginal Distributions Of Crossing Time And Renewal Numbers Related To Two-Stage Erlang Processes, Mir Ghulam Hyder Talpur, Iffat Zamir, M. Masoom Ali

Journal of Modern Applied Statistical Methods

The two dimensional marginal transform, probability density and cumulative probability distribution functions for the random variables TξN (time taken by servers during vacations), ξN (number of vacations taken by servers) and Nη (number of customers or units arriving in the system) are derived by taking combinations of these random variables. One random variable is controlled at one time to determine the effect of the other two random variables simultaneously.

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Bootstrap Confidence Intervals And Coverage Probabilities Of Regression Parameter Estimates Using Trimmed Elemental Estimation, Matthew Hall, Matthew S. Mayo Nov 2008

Bootstrap Confidence Intervals And Coverage Probabilities Of Regression Parameter Estimates Using Trimmed Elemental Estimation, Matthew Hall, Matthew S. Mayo

Journal of Modern Applied Statistical Methods

Mayo and Gray introduced the leverage residual-weighted elemental (LRWE) classification of regression estimators and a new method of estimation called trimmed elemental estimation (TEE), showing the efficiency and robustness of TEE point estimates. Using bootstrap methods, properties of various trimmed elemental estimator interval estimates to allow for inference are examined and estimates with ordinary least squares (OLS) and least sum of absolute values (LAV) are compared. Confidence intervals and coverage probabilities for the estimators using a variety of error distributions, sample sizes, and number of parameters are examined. To reduce computational intensity, randomly selecting elemental subsets to calculate the parameter …

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Robust Predictive Inference For Multivariate Linear Models With Elliptically Contoured Distribution Using Bayesian, Classical And Structural Approaches, B. M. Golam Kibria Nov 2008

Robust Predictive Inference For Multivariate Linear Models With Elliptically Contoured Distribution Using Bayesian, Classical And Structural Approaches, B. M. Golam Kibria

Journal of Modern Applied Statistical Methods

Predictive distributions of future response and future regression matrices under multivariate elliptically contoured distributions are discussed. Under the elliptically contoured response assumptions, these are identical to those obtained under matric normal or matric-t errors using structural, Bayesian with improper prior, or classical approaches. This gives inference robustness with respect to departure from the reference case of independent sampling from the matric normal or matric t to multivariate elliptically contoured distributions. The importance of the predictive distribution for skewed elliptical models is indicated; the elliptically contoured distribution, as well as matric t distribution, have significant applications in statistical practices.

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Delete And Revise Procedures For Two-Stage Short-Run Control Charts, Matthew E. Elam Nov 2008

Delete And Revise Procedures For Two-Stage Short-Run Control Charts, Matthew E. Elam

Journal of Modern Applied Statistical Methods

This article investigates the effect different delete and revise procedures have on the performance of twostage short-run control charting methodology in the second stage of its two stage procedure. Five variables control chart combinations, six delete and revise procedures, and various out-of-control situations in both stages are considered.

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A Methodology To Improve Pci Use In Industry, Milind A. Phadnis, Matthew E. Elam Nov 2008

A Methodology To Improve Pci Use In Industry, Milind A. Phadnis, Matthew E. Elam

Journal of Modern Applied Statistical Methods

This article presents the development of a methodology using decision trees to resolve issues in industry with using process capability indices (PCIs). The methodology forms the structure of a prototype decision support system (PDSS) for PCI selection, calculation, and interpretation. Download instructions for the PDSS are available at http://program.20m.com.

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The Multinomial Regression Modeling Of The Cause-Of-Death Mortality Of The Oldest Old In The U.S., Dudley L. Poston Jr., Hosik Min Nov 2008

The Multinomial Regression Modeling Of The Cause-Of-Death Mortality Of The Oldest Old In The U.S., Dudley L. Poston Jr., Hosik Min

Journal of Modern Applied Statistical Methods

The statistical modeling of the causes of death of the oldest old (persons aged 80 and over) in the U.S. in 2001 was conducted in this article. Data were analyzed using a multinomial logistic regression model (MNLM) because multiple causes of death are coded on death certificates and the codes are nominal. The percentage distribution of the 10 major causes of death among the oldest old was first examined; we next estimated a multinomial logistic regression equation to predict the likelihood of elders dying of one of the causes of death compared to dying of an “other cause.” The independent …

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Frequency Domain Modeling With Piecewise Constant Spectra, Erhard Reschenhofer Nov 2008

Frequency Domain Modeling With Piecewise Constant Spectra, Erhard Reschenhofer

Journal of Modern Applied Statistical Methods

Using piecewise constant functions as models for the spectral density of the differenced log real U.S. GDP it was found that these models have the capacity to compete with the spectral densities implied by ARMA models. According to AIC and BIC the piecewise constant spectral densities are superior to ARMA.

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Correlation Between The Sample Mean And Sample Variance, Ramalingam Shanmugam Nov 2008

Correlation Between The Sample Mean And Sample Variance, Ramalingam Shanmugam

Journal of Modern Applied Statistical Methods

This article obtains a general formula to find the correlation coefficient between the sample mean and variance. Several particular results for major non-normal distributions are extracted to help students in classroom, clients during statistical consulting service.

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Size-Biased Generalized Negative Binomial Distribution, Khurshid Ahmad Mir Nov 2008

Size-Biased Generalized Negative Binomial Distribution, Khurshid Ahmad Mir

Journal of Modern Applied Statistical Methods

A size biased generalized negative binomial distribution (SBGNBD) is defined and a recurrence relationship for the moments of SBGNBD is established. The Bayes’ estimator for a parametric function of one parameter when two other parameters of a known size-biased generalized negative binomial distribution is derived. Prior information on one parameter is given by a beta distribution and the parameters in the prior distribution are assigned by computer using Monte Carlo and R-software.

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Non-Parametric Quantile Selection For Extreme Distributions, Wan Zawiah Wan Zin, Abdul Aziz Jemain Nov 2008

Non-Parametric Quantile Selection For Extreme Distributions, Wan Zawiah Wan Zin, Abdul Aziz Jemain

Journal of Modern Applied Statistical Methods

The objective is to select the best non-parametric quantile estimation method for extreme distributions. This serves as a starting point for further research in quantile application such as in parameter estimation using LQ-moments method. Thirteen methods of non-parametric quantile estimation were applied on six types of extreme distributions and their efficiencies compared. Monte Carlo methods were used to generate the results, which showed that the method of Weighted Kernel estimator of Type 1 was more efficient than the other methods in many cases.

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