Digital Commons Network^™

Interaction Graphs For 4R2N-P Fractional Factorial Designs, M. L. Aggarwal, S. Roy Chowdhury, Anita Bansal, Neena Mital

Journal of Modern Applied Statistical Methods

Interaction graphs have been developed for two-level and three-level fractional factorial designs under different design criteria. A catalogue is presented of all possible non-isomorphic interaction graphs for 4^r2^n-p (r=1; n=2,…, 10; p=1,…,8 and r=2; n=1,…, 7; p=1,…,7) fractional factorial designs, and nonisomorphic interaction graphs for asymmetric fractional factorial designs under the concept of combined array.

Jmasm24: Numerical Computing For Third-Order Power Method Polynomials (Excel), Todd C. Headrick

Journal of Modern Applied Statistical Methods

The power method polynomial transformation is a popular procedure used for simulating univariate and multivariate non-normal distributions. It requires software that solves simultaneous nonlinear equations. Potential users of the power method may not have access to commercial software packages (e.g., Mathematica, Fortran). Therefore, algorithms are presented in the more commonly available Excel 2003 spreadsheets. The algorithms solve for (1) coefficients for polynomials of order three, (2) intermediate correlations and Cholesky factorizations for multivariate data generation, and (3) the values of skew and kurtosis for determining if a transformation will produce a valid power method probability density function (pdf). The Excel …

A Discretized Approach To Flexibly Fit Generalized Lambda Distributions To Data, Steve Su

Journal of Modern Applied Statistical Methods

This article presents a flexible approach to fit statistical distribution to data. It optimizes the bin-width of data histogram to find a suitable generalized lambda distribution. In addition to the default optimization, this approach provides additional flexibility akin to the concepts of loess and kernel smoothing, which allow the users to determine the amount of details they would like to smooth over the data. The approach presented in this article will allow users to visually compare and choose the parameters of generalized lambda distribution that best suit their purposes of study.

A Single, Powerful, Nonparametric Statistic For Continuous-Data Telecommunications Parity Testing, J. D. Opdyke

Journal of Modern Applied Statistical Methods

Since the enactment of the Telecommunications Act of 1996, extensive expert testimony has justified use of the modified t statistic (Brownie et al., 1990) for performing two-sample hypothesis tests comparing Bell companies’ CLEC and ILEC performance measurement data (known as parity testing). However, Opdyke (Telecommunications Policy, 2004) demonstrated this statistic to be potentially manipulable and to have literally zero power to detect inferior CLEC service provision under a wide range of relevant data conditions. This article develops a single, nonparametric statistic that is easily implemented (i.e., not computationally intensive) and typically provides dramatic power gains over the modified t while …

Testing Goodness Of Fit Of The Geometric Distribution: An Application To Human Fecundability Data, Sudhir R. Paul

Journal of Modern Applied Statistical Methods

A measure of reproduction in human fecundability studies is the number of menstrual cycles required to achieve pregnancy which is assumed to follow a geometric distribution with parameter p. Tests of heterogeneity in the fecundability data through goodness of fit tests of the geometric distribution are developed, along with a likelihood ratio test statistic and a score test statistic. Simulations show both are liberal, and empirical level of the likelihood ratio statistic is larger than that of the score test statistic. A power comparison shows that the likelihood ratio test has a power advantage. A bootstrap p-value procedure using the …

Estimation Of Process Variances In Robust Parameter Designs, T. K. Mak, Fassil Nebebe

Journal of Modern Applied Statistical Methods

The modeling of variation through interactions is appealing in crossed array design as it leads to greater robustness to certain type of model misspecification. As an alternative to signal-to-noise analysis, a new, systematic method based on Taguchi type crossed array design is given. It is shown in this article that when fractional factorial design is used for the outer array, the crossed array design is not robust to the presence of noise-noise interactions and a method of rectifying the problem is suggested.

Simulation Procedure In Periodic Cancer Screening Trials, Ioana Barnicescu, Ricolindo L. Cariño

Journal of Modern Applied Statistical Methods

A general simulation procedure is described to validate model fitting algorithms for complex likelihood functions that are utilized in periodic cancer screening trials. Although screening programs have existed for a few decades, there are still many unsolved problems, such as how age or hormone affects the screening sensitivity, the sojourn time in the preclinical state, and the transition probability from diseasefree state to the preclinical state. Simulations are needed to check reliability or validity of the likelihood function combined with the associated effect functions. One bottleneck in the simulation procedure is the very time consuming calculations of the maximum likelihood …

Inference On (Y < X) In A Pareto Distribution, M. Masoom Ali, Jungsoo Woo

Journal of Modern Applied Statistical Methods

Inference on the reliability R = P(Y < X) in a Pareto distribution with a known scale parameter is considered. Point estimates and confidence intervals of R are obtained a test of hypothesis is also considered.

Nonparametric Pooling And Testing Of Preference Ratings For Full-Profile Conjoint Analysis Experiments, Rosa Arboretti G., Marco Marozzi, Luigi Salmaso

Journal of Modern Applied Statistical Methods

The problem of pooling customer preference ratings within a conjoint analysis experiment has been addressed. A method based on the nonparametric combination of rankings has been proposed to compete with the usual method based on the arithmetic mean. This method is nonparametric with respect to the underlying dependence structure and so no dependence model must be assumed. The two methods have been compared using Spearman’s rank correlation coefficient and related test. Moreover, a further nonparametric testing method has been considered and proposed; this method takes both correlation and distance between ranks into account. By means of a simulation study it …

Statistical Pronouncements Iv, Jmasm Editors

Journal of Modern Applied Statistical Methods

No abstract provided.

Jmasm20: Exact Permutation Critical Values For The Kruskal-Wallis One-Way Anova, Justice I. Odiase, Sunday M. Ogbonmwan

Journal of Modern Applied Statistical Methods

The exhaustive enumeration of all the permutations of the observations in an experiment is the only possible way of truly constructing exact tests of significance. The permutation paradigm requires no distributional assumptions and works well with values that are normal, almost normal and non-normally distributed. The Kruskal-Wallis test does not require the assumptions that the samples are from normal populations and that the samples have the same standard deviation. In this article, the exact permutation distribution of the Kruskal-Wallis test statistic is generated empirically by actually obtaining all the distinct permutations of an experiment. The tables of exact critical values …

Statistical Model And Estimation Of The Optimum Price For A Chain Of Price Setting Firms, Chengjie Xiong, Kejun Zhu

Journal of Modern Applied Statistical Methods

A stochastic approach is used to model the economics of a chain of price setting firms. It is assumed that these firms have fixed capacities in their products, but random demands for their products. The optimum price, the optimum revenue, and the expected marginal revenue at a given price are investigated. The method of maximum likelihood is used to provide both point and confidence interval estimates. The coverage probabilities of confidence interval estimates based on a simulation study are presented.

The Influence Of Reliability On Four Rules For Determining The Number Of Components To Retain, Gibbs Y. Kanyongo

Journal of Modern Applied Statistical Methods

Imperfectly reliable scores impact the performance of factor analytic procedures. A series of Monte Carlo studies was conducted to generate scores with known component structure from population matrices with varying levels of reliability. The scores were submitted to four procedures: Kaiser rule, scree plot, parallel analysis, and modified Horn’s parallel analysis to find if each procedure accurately determines the number of components at the different reliability levels. The performance of each procedure was judged by the percentage of the number of times that the procedure was correct and the mean components that each procedure extracted in each cell. Generally, the …

Corrections For Type I Error In Social Science Research: A Disconnect Between Theory And Practice, Kenneth Lachlan, Patric R. Spence

Journal of Modern Applied Statistical Methods

Type I errors are a common problem in factorial ANOVA and ANOVA based analyses. Despite decades of literature offering solutions to the Type I error problems associated with multiple significance tests, simple solutions such as Bonferroni corrections have been largely ignored by social scientists. To examine this discontinuity between theory and practice, a content analysis was performed on 5 flagship social science journals. Results indicate that corrections for Type I error are seldom utilized, even in designs so complicated as to almost guarantee erroneous rejection of null hypotheses.

Model Selection Of Meat Demand System Using The Rotterdam Model And The Almost Ideal Demand System (Aids), Maria Divina S. Paraguas, Anton Abdulbasah Kamil

Journal of Modern Applied Statistical Methods

Aggregated time series data for differentiated meat products namely, beef, pork, poultry, and mutton were used to estimate and analyze Malaysian market demand for meats. The study aimed to select the most appropriate demand model between the equally popular Rotterdam model and the first difference Linear Approximate Almost Ideal Demand System (LA/AIDS) model by using a non-nested test. Both models were accepted, but further diagnostic tests revealed that the first difference LA/AIDS represents more appropriately the Malaysian market demand for meat than the Rotterdam model. Also, the elasticities from the first difference LA/AIDS were found to be more reliable than …

Statistical Methods And Artificial Neural Networks, Mammadagha Mammadov, Berna Yazici, Şenay Yolaçan, Atilla Aslanargun, Ali Fuat YüZer, Embiya Ağaoğlu

Journal of Modern Applied Statistical Methods

Artificial Neural Networks and statistical methods are applied on real data sets for forecasting, classification, and clustering problems. Hybrid models for two components are examined on different data sets; tourist arrival forecasting to Turkey, macro-economic problem on rescheduling of the countries’ international debts, and grouping twenty-five European Union member and four candidate countries according to macro-economic indicators.

Jmasm25: Computing Percentiles Of Skew-Normal Distributions, Sikha Bagui, Subhash Bagui

Journal of Modern Applied Statistical Methods

An algorithm and code is provided for computing percentiles of skew-normal distributions with parameter λ using Monte Carlo methods. A critical values table was created for various parameter values of λ at various probability levels of α . The table will be useful to practitioners as it is not available in the literature.

Applications Of Some Improved Estimators In Linear Regression, B. M. Golam Kibria

Journal of Modern Applied Statistical Methods

The problem of estimation of the regression coefficients under multicollinearity situation for the restricted linear model is discussed. Some improve estimators are considered, including the unrestricted ridge regression estimator (URRE), restricted ridge regression estimator (RRRE), shrinkage restricted ridge regression estimator (SRRRE), preliminary test ridge regression estimator (PTRRE), and restricted Liu estimator (RLIUE). The were compared based on the sampling variance-covariance criterion. The RRRE dominates other ridge estimators when the restriction does or does not hold. A numerical example was provided. The RRRE performed equivalently or better than the RLIUE in the sense of having smaller sampling variance.

Analyzing Panel Count Data With Informative Observation Times, Chiung-Yu Huang, Mei-Cheng Wang, Ying Zhang

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we study panel count data with informative observation times. We assume nonparametric and semiparametric proportional rate models for the underlying recurrent event process, where the form of the baseline rate function is left unspecified and a subject-specific frailty variable inflates or deflates the rate function multiplicatively. The proposed models allow the recurrent event processes and observation times to be correlated through their connections with the unobserved frailty; moreover, the distributions of both the frailty variable and observation times are considered as nuisance parameters. The baseline rate function and the regression parameters are estimated by maximizing a conditional …

A Fine-Scale Linkage Disequilibrium Measure Based On Length Of Haplotype Sharing, Yan Wang, Lue Ping Zhao, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

High-throughput genotyping technologies for single nucleotide polymorphisms (SNP) have enabled the recent completion of the International HapMap Project (Phase I), which has stimulated much interest in studying genome-wide linkage disequilibrium (LD) patterns. Conventional LD measures, such as D' and r-square, are two-point measurements, and their relationship with physical distance is highly noisy. We propose a new LD measure, defined in terms of the correlation coefficient for shared haplotype lengths around two loci, thereby borrowing information from multiple loci. A U-statistic-based estimator of the new LD measure, which takes into consideration the dependence structure of the observed data, is developed and …

Population Intervention Models In Causal Inference, Alan E. Hubbard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a] treatment variable or risk variable on the distribution of a disease in a population. These models, as originally introduced by Robins (e.g., Robins (2000a), Robins (2000b), van der Laan and Robins (2002)), model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates, and its dependence on treatment. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject's treatment and covariate history are measured over time, and an outcome is recorded at …

Gauss-Seidel Estimation Of Generalized Linear Mixed Models With Application To Poisson Modeling Of Spatially Varying Disease Rates, Subharup Guha, Louise Ryan

Harvard University Biostatistics Working Paper Series

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases.

This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM …

Designed Extension Of Survival Studies: Application To Clinical Trials With Unrecognized Heterogeneity, Yi Li, Mei-Chiung Shih, Rebecca A. Betensky

Harvard University Biostatistics Working Paper Series

It is well known that unrecognized heterogeneity among patients, such as is conferred by genetic subtype, can undermine the power of randomized trial, designed under the assumption of homogeneity, to detect a truly beneficial treatment. We consider the conditional power approach to allow for recovery of power under unexplained heterogeneity. While Proschan and Hunsberger (1995) confined the application of conditional power design to normally distributed observations, we consider more general and difficult settings in which the data are in the framework of continuous time and are subject to censoring. In particular, we derive a procedure appropriate for the analysis of …

Computational Techniques For Spatial Logistic Regression With Large Datasets, Christopher J. Paciorek, Louise Ryan

Harvard University Biostatistics Working Paper Series

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation.

A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial …

Student Fact Book, Fall 2005, Twenty-Ninth Annual Edition, Wright State University, Office Of Student Information Systems, Wright State University

Wright State University Student Fact Books

The student fact book has general demographic information on all students enrolled at Wright State University for Fall Quarter, 2005.

Is The Number Of Sick Persons In A Cohort Constant Over Time?, Paula Diehr, Ann Derleth, Anne Newman, Liming Cai

UW Biostatistics Working Paper Series

Objectives: To estimate the number of persons in a cohort who are sick, over time.

Methods: We calculated the number of sick persons in the Cardiovascular Health Study (CHS), a cohort study of older adults followed up to 14 years, using eight definitions of “healthy” and “sick”. We projected the number in each health state over time for a birth cohort.

Results: The number of sick persons in CHS was approximately constant for 14 years, for all definitions of “sick”. The estimated number of sick persons in the birth cohort was approximately constant from ages 55-75, after which it decreased. …

Remarks On Risk-Sensitive Control Problems, José Luis Menaldi, Maurice Robin

Mathematics Faculty Research Publications

The main purpose of this paper is to investigate the asymptotic behavior of the discounted risk-sensitive control problem for periodic diffusion processes when the discount factor α goes to zero. If u_α(θ, x) denotes the optimal cost function, being the risk factor, then it is shown that lim_α→0αu_α(θ, x) = ξ(θ) where ξ(θ) is the average on ]0, θ[ of the optimal cost of the (usual) in nite horizon risk-sensitive control problem.

Why Are So Many People Challenging Board Of Immigration Appeals Decisions In Federal Court? An Empirical Analysis Of The Recent Surge In Petitions For Review, John R.B. Palmer, Stephen W. Yale-Loehr, Elizabeth Cronin

Cornell Law Faculty Publications

No abstract provided.

On The Synthesis Of Microarray Experiments, Robert Gentleman, Markus Ruschhaupt, Wolfgang Huber

Bioconductor Project Working Papers

With many different investigators studying the same disease and with a strong commitment to publish supporting data in the scientific community, there are often many different datasets available for any given disease. Hence there is substantial interest in finding methods for combining these datasets to provide better and more detailed understanding of the underlying biology. We consider the synthesis of different microarray data sets using a random effects paradigm and demonstrate how relatively standard statistical approaches yield good results. We identify a number of important and substantive areas which require further investigation.

Feature-Specific Penalized Latent Class Analysis For Genomic Data, E. Andres Houseman, Brent A. Coull, Rebecca A. Betensky

Harvard University Biostatistics Working Paper Series

No abstract provided.