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Articles 751 - 780 of 1162

Full-Text Articles in Statistics and Probability

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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Analyzing Incomplete Categorical Data: Revisiting Maximum Likelihood Estimation (Mle) Procedure, Hoo Ling Ping, M. Ataharul Islam Nov 2008

Analyzing Incomplete Categorical Data: Revisiting Maximum Likelihood Estimation (Mle) Procedure, Hoo Ling Ping, M. Ataharul Islam

Journal of Modern Applied Statistical Methods

Incomplete data poses formidable difficulties in the application of statistical techniques and requires special procedures to handle. The most common ways to solve this problem are by ignoring, truncating, censoring or collapsing those data, but these may lead to inappropriate conclusions because those data might contain important information. Most of the research for estimating cell probabilities involving incomplete categorical data is based on the EM algorithm. A likelihood approach is employed for estimating cell probabilities for missing values and makes comparisons between maximum likelihood estimation (MLE) and the EM algorithm. The MLE can provide almost the same estimates as that …

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Adaptive Estimation Of Heteroscedastic Linear Regression Model Using Probability Weighted Moments, Faqir Muhammad, Muhammad Aslam, G.R. Pasha Nov 2008

Adaptive Estimation Of Heteroscedastic Linear Regression Model Using Probability Weighted Moments, Faqir Muhammad, Muhammad Aslam, G.R. Pasha

Journal of Modern Applied Statistical Methods

An adaptive estimator is presented by using probability weighted moments as weights rather than conventional estimates of variances for unknown heteroscedastic errors while estimating a heteroscedastic linear regression model. Empirical studies of the data generated by simulations for normal, uniform, and logistically distributed error terms support our proposed estimator to be quite efficient, especially for small samples.

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Construction Of Insurance Scoring System Using Regression Models, Noriszura Ismail, Abdul Aziz Jemain Nov 2008

Construction Of Insurance Scoring System Using Regression Models, Noriszura Ismail, Abdul Aziz Jemain

Journal of Modern Applied Statistical Methods

This study suggests the regression models of Lognormal, Normal and Gamma for constructing insurance scoring system. The main advantage of a scoring system is that it can be used by insurers to differentiate between high and low risks insureds, thus allowing the profitability of insureds to be predicted.

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Type I Error Rates Of The Kenward-Roger F-Test For A Split-Plot Design With Missing Values And Non-Normal Data, Miguel A. Padilla, Youngkyoung Min, Guili Zhang Nov 2008

Type I Error Rates Of The Kenward-Roger F-Test For A Split-Plot Design With Missing Values And Non-Normal Data, Miguel A. Padilla, Youngkyoung Min, Guili Zhang

Journal of Modern Applied Statistical Methods

The Type I error of the Kenward-Roger (KR) F-test was assessed through a simulation study for a between- by within-subjects split-plot design with non-normal ignorable missing data. The KR-test for the between- and within-subjects main effect was robust under all simulation variables investigated and when the data were missing completely at random (MCAR). This continued to hold for the between-subjects main effect when data were missing at random (MAR). For the interaction, the KR F-test performed fairly well at controlling Type I under MCAR and the simulation variables investigated. However, under MAR, the KR F-test for the …

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Constructing Confidence Intervals For Spearman’S Rank Correlation With Ordinal Data: A Simulation Study Comparing Analytic And Bootstrap Methods, John Ruscio Nov 2008

Constructing Confidence Intervals For Spearman’S Rank Correlation With Ordinal Data: A Simulation Study Comparing Analytic And Bootstrap Methods, John Ruscio

Journal of Modern Applied Statistical Methods

Research shows good probability coverage using analytic confidence intervals (CIs) for Spearman’s rho with continuous data, but poorer coverage with ordinal data. A simulation study examining the latter case replicated prior results and revealed that coverage of bootstrap CIs was usually as good or better than coverage of analytic CIs.

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An Optimum Allocation With A Family Of Estimators Using Auxiliary Information In Sample Survey, Gajendra K. Vishwakarma, Housila P. Singh Nov 2008

An Optimum Allocation With A Family Of Estimators Using Auxiliary Information In Sample Survey, Gajendra K. Vishwakarma, Housila P. Singh

Journal of Modern Applied Statistical Methods

The problem of obtaining optimum allocation using auxiliary information in stratified random sampling. An optimum allocation with a family of estimators is obtained and its efficiency is compared with that of Neyman allocation based on Srivastava (1971) class of estimators and the optimum allocation suggested by Zaidi et al., (1989). It is shown that the proposed allocation is better in the sense having smaller variance compared to other optimum allocation.

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On Some Properties Of Quasi-Negative-Binomial Distribution And Its Applications, Anwar Hassan, Sheikh Bilal Nov 2008

On Some Properties Of Quasi-Negative-Binomial Distribution And Its Applications, Anwar Hassan, Sheikh Bilal

Journal of Modern Applied Statistical Methods

The quasi-negative-binomial distribution was applied to queuing theory for determining the distribution of total number of customers served before the queue vanishes under certain assumptions. Some structural properties (probability generating function, convolution, mode and recurrence relation) for the moments of quasi-negative-binomial distribution are discussed. The distribution’s characterization and its relation with other distributions were investigated. A computer program was developed using R to obtain ML estimates and the distribution was fitted to some observed sets of data to test its goodness of fit.

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Confidence Intervals Based On Robust Estimators, Meral Cetin, Serpil Aktas May 2008

Confidence Intervals Based On Robust Estimators, Meral Cetin, Serpil Aktas

Journal of Modern Applied Statistical Methods

Classical estimation of confidence intervals based on the sample mean and variance is sensitive to outliers. Robust methods were proposed for reducing the influence of outliers. The Minimum Volume Ellipsoid estimator (MVE), having a high breakdown point, is one of the robust estimators for location and scale parameters. The robust confidence interval for location parameter is constructed based on the MVE, and compared with the proposed robust confidence interval estimation methods. The performance of the robust confidence interval based on MVE is illustrated with a simulation study. The lengths of 100(1-α)% confidence intervals were investigated.

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Using Connectionist Models To Evaluate Examinees’ Response Patterns To Achievement Tests, Mark J. Gierl, Ying Cui, Steve Hunka May 2008

Using Connectionist Models To Evaluate Examinees’ Response Patterns To Achievement Tests, Mark J. Gierl, Ying Cui, Steve Hunka

Journal of Modern Applied Statistical Methods

The attribute hierarchy method (AHM) applied to assessment engineering is described. It is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive …

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Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina May 2008

Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina

Journal of Modern Applied Statistical Methods

The coverage performance of the confidence intervals (CIs) for the Root Mean Square Standardized Effect Size (RMSSE) was investigated in a balanced, one-way, fixed-effects, between-subjects ANOVA design. The noncentral F distribution-based and the percentile bootstrap CI construction methods were compared. The results indicated that the coverage probabilities of the CIs for RMSSE were not adequate.

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An Evaluation Of Standard, Alternative, And Robust Slope Test Strategies, Tim Moses, Alan Klockars May 2008

An Evaluation Of Standard, Alternative, And Robust Slope Test Strategies, Tim Moses, Alan Klockars

Journal of Modern Applied Statistical Methods

The robustness and power of nine strategies for testing the differences between two groups’ regression slopes under nonnormality and residual variance heterogeneity are compared. The results showed that three most robust slope test strategies were the combination of the trimmed and Winsorized slopes with the James second order test, the combination of Theil-Sen with James, and Theil-Sen with percentile bootstrapping. The slope tests based on Theil-Sen slopes were more powerful than those based on trimmed and Winsorized slopes.

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Second-Order Latent Growth Models With Shifting Indicators, Gregory R. Hancock, Michelle M. Buehl May 2008

Second-Order Latent Growth Models With Shifting Indicators, Gregory R. Hancock, Michelle M. Buehl

Journal of Modern Applied Statistical Methods

Second-order latent growth models assess longitudinal change in a latent construct, typically employing identical manifest variables as indicators across time. However, the same indicators may be unavailable and/or inappropriate for all time points. This article details methods for second-order growth models in which constructs’ indicators shift over time.

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Selection Of Non-Regular Fractional Factorial Designs When Some Two-Factor Interactions Are Important, Weiming Ke, Rui Yao May 2008

Selection Of Non-Regular Fractional Factorial Designs When Some Two-Factor Interactions Are Important, Weiming Ke, Rui Yao

Journal of Modern Applied Statistical Methods

A new method is proposed for selecting the optimal non-regular fractional factorial designs in the situation when some two-factor interactions are potentially important. Searching for the best designs according to this method is discussed and some results for the Plackett-Burman design of 12 runs are presented.

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A Weighted Moving Average Process For Forecasting, Shou Hsing Shih, Chris P. Tsokos May 2008

A Weighted Moving Average Process For Forecasting, Shou Hsing Shih, Chris P. Tsokos

Journal of Modern Applied Statistical Methods

The object of the present study is to propose a forecasting model for a nonstationary stochastic realization. The subject model is based on modifying a given time series into a new k-time moving average time series to begin the development of the model. The study is based on the autoregressive integrated moving average process along with its analytical constrains. The analytical procedure of the proposed model is given. A stock XYZ selected from the Fortune 500 list of companies and its daily closing price constitute the time series. Both the classical and proposed forecasting models were developed and a comparison …

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Comparing Different Methods For Multiple Testing In Reaction Time Data, Massimiliano Pastore, Massimo Nucci, Giovanni Galfano May 2008

Comparing Different Methods For Multiple Testing In Reaction Time Data, Massimiliano Pastore, Massimo Nucci, Giovanni Galfano

Journal of Modern Applied Statistical Methods

Reaction times were simulated for examining the power of six methods for multiple testing, as a function of sample size and departures from normality. Power estimates were low for all methods for non-normal distributions. With normal distributions, even for small sample sizes, satisfactory power estimates were observed, especially for FDR-based procedures.

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Two-Stage Short-Run (X, Mr) Control Charts, Matthew E. Elam, Kenneth E. Case May 2008

Two-Stage Short-Run (X, Mr) Control Charts, Matthew E. Elam, Kenneth E. Case

Journal of Modern Applied Statistical Methods

This article is the first in a series of two articles that applies two-stage short-run control charting to (X, MR) charts. Theory is developed and then used to derive the control chart factor equations. In the sequel, the control chart factor calculations are computerized and an example is presented.

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