Statistics and Probability Commons^™

Ordinal Regression Analysis: Fitting The Proportional Odds Model Using Stata, Sas And Spss, Xing Liu

Journal of Modern Applied Statistical Methods

Researchers have a variety of options when choosing statistical software packages that can perform ordinal logistic regression analyses. However, statistical software, such as Stata, SAS, and SPSS, may use different techniques to estimate the parameters. The purpose of this article is to (1) illustrate the use of Stata, SAS and SPSS to fit proportional odds models using educational data; and (2) compare the features and results for fitting the proportional odds model using Stata OLOGIT, SAS PROC LOGISTIC (ascending and descending), and SPSS PLUM. The assumption of the proportional odds was tested, and the results of the fitted models were …

Estimation Of The Standardized Mean Difference For Repeated Measures Designs, Lindsey J. Wolff Smith, S. Natasha Beretvas

Journal of Modern Applied Statistical Methods

This simulation study modified the repeated measures mean difference effect size, d⁼_RM , for scenarios with unequal pre- and post-test score variances. Relative parameter and SE bias were calculated for dRM ≠ versus dRM = . Results consistently favored d^≠_RM over d⁼_RM with worse positive parameter and negative SE bias identified for d⁼_RM for increasingly heterogeneous variance conditions.

Estimating The Parameters Of Rayleigh Cumulative Exposure Model In Simple Step-Stress Testing. Natasha Beretvas Is An, Mohammed Al-Haj Ebrahem, Abedel-Qader Al-Masri

Journal of Modern Applied Statistical Methods

Assumes the life distribution of a test unit for any stress follows a Rayleigh distribution with scale parameterθ , and that Ln(θ ) is a linear function of the stress level. Maximum likelihood estimators of the parameters under a cumulative exposure model are obtained. The approximate variance estimates obtained from the asymptotic normal distribution of the maximum likelihood estimators are used to construct confidence intervals for the model parameters. A simulation study was conducted to study the performance of the estimators. Simulation results showed that in terms of bias, mean squared error, attainment of the nominal confidence level, symmetry …

Test For The Equality Of The Number Of Signals, Madhusudan Bhandary, Debasis Kundu

Journal of Modern Applied Statistical Methods

A likelihood ratio test for testing the equality of the ranks of two non-negative definite covariance matrices arising in the area of signal processing is derived. The asymptotic distribution of the test statistic follows a Chi-square distribution from the general theory of likelihood ratio test.

Jmasm29: Dominance Analysis Of Independent Data (Fortran), Du Feng, Normal Cliff

Journal of Modern Applied Statistical Methods

A Fortran 77 program is provided for an ordinal dominance analysis of independent two-group comparisons. The program calculates the ordinal statistic, d, and statistical inferences about δ. The source codes and an executable file are available at http://www.depts.ttu.edu/hdfs/feng.php.

Analysis Of Multifactor Experimental Designs, Phillip I. Good

Journal of Modern Applied Statistical Methods

In the one-factor case, Good and Lunneborg (2006) showed that the permutation test is superior to the analysis of variance. In the multi-factor case, simulations reveal the reverse is true. The analysis of variance is remarkably robust against departures from normality including instances in which data is drawn from mixtures of normal distributions or from Weibull distributions. The traditional permutation test based on all rearrangements of the data labels is not exact and is more powerful that the analysis of variance only for 2xC designs or when there is only a single significant effect. Permutation tests restricted to synchronized permutations …

Assessing Trends: Monte Carlo Trials With Four Different Regression Methods, Daniel R. Thompson

Journal of Modern Applied Statistical Methods

Ordinary Least Squares (OLS), Poisson, Negative Binomial, and Quasi-Poisson Regression methods were assessed for testing the statistical significance of a trend by performing 10,000 simulations. The Poisson method should be used when data follow a Poisson distribution. The other methods should be used when data follow a normal distribution.

Approximate Bayesian Confidence Intervals For The Mean Of A Gaussian Distribution Versus Bayesian Models, Vincent A. R. Camara

Journal of Modern Applied Statistical Methods

This study obtained and compared confidence intervals for the mean of a Gaussian distribution. Considering the square error and the Higgins-Tsokos loss functions, approximate Bayesian confidence intervals for the mean of a normal population are derived. Using normal data and SAS software, the obtained approximate Bayesian confidence intervals were compared to a published Bayesian model. Whereas the published Bayesian method is sensitive to the choice of the hyper-parameters and does not always yield the best confidence intervals, it is shown that the proposed approximate Bayesian approach relies only on the observations and often performs better.

Semi-Parametric Of Sample Selection Model Using Fuzzy Concepts, L. Muhamad Safiih, A. A. Kamil, M. T. Abu Osman

Journal of Modern Applied Statistical Methods

The sample selection model has been studied in the context of semi-parametric methods. With the deficiencies of the parametric model, such as inconsistent estimators, semi-parametric estimation methods provide better alternatives. This article focuses on the context of fuzzy concepts as a hybrid to the semiparametric sample selection model. The better approach when confronted with uncertainty and ambiguity is to use the tools provided by the theory of fuzzy sets, which are appropriate for modeling vague concepts. A fuzzy membership function for solving uncertainty data of a semi-parametric sample selection model is introduced as a solution to the problem.

Performance Ratings Of An Autocovariance Base Estimator (Abe) In The Estimation Of Garch Model Parameters When The Normality Assumption Is Invalid, Daniel Eni

Journal of Modern Applied Statistical Methods

The performance of an autocovariance base estimator (ABE) for GARCH models against that of the maximum likelihood estimator (MLE) if a distribution assumption is wrongly specified as normal was studied. This was accomplished by simulating time series data that fits a GARCH model using the Log normal and t-distributions with degrees of freedom of 5, 10 and 15. The simulated time series was considered as the true probability distribution, but normality was assumed in the process of parameter estimations. To track consistency, sample sizes of 200, 500, 1,000 and 1,200 were employed. The two methods were then used to analyze …

A Linear B-Spline Threshold Dose-Response Model With Dose-Specific Response Variation Applied To Developmental Toxicity Studies, Chin-Shang Li, Daniel L. Hunt

Journal of Modern Applied Statistical Methods

A linear B-spline function was modified to model dose-specific response variation in developmental toxicity studies. In this new model, response variation is assumed to differ across dose groups. The model was applied to a developmental toxicity study and proved to be significant over the previous model of singular response variation.

Jmasm28: Gibbs Sampling For 2pno Multi-Unidimensional Item Response Theory Models (Fortran), Yanyan Sheng, Todd C. Headrick

Journal of Modern Applied Statistical Methods

A Fortran 77 subroutine is provided for implementing the Gibbs sampling procedure to a multiunidimensional IRT model for binary item response data with the choice of uniform and normal prior distributions for item parameters. In addition to posterior estimates of the model parameters and their Monte Carlo standard errors, the algorithm also estimates the correlations between distinct latent traits. The subroutine requires the user to have access to the IMSL library. The source code is available at http://www.siuc.edu/~epse1/sheng/Fortran/MUIRT/GSMU2.FOR. An executable file is also provided for download at http://www.siuc.edu/~epse1/sheng/Fortran/MUIRT/EXAMPLE.zip to demonstrate the implementation of the algorithm on simulated data.

Markov Modeling Of Breast Cancer, Chunling Cong, Chris P. Tsokos

Journal of Modern Applied Statistical Methods

Previous work with respect to the treatments and relapse time for breast cancer patients is extended by applying a Markov chain to model three different types of breast cancer patients: alive without ever having relapse, alive with relapse, and deceased. It is shown that combined treatment of tamoxifen and radiation is more effective than single treatment of tamoxifen in preventing the recurrence of breast cancer. However, if the patient has already relapsed from breast cancer, single treatment of tamoxifen would be more appropriate with respect to survival time after relapse. Transition probabilities between three stages during different time periods, 2-year, …

Impact Of Rank-Based Normalizing Transformations On The Accuracy Of Test Scores, Shira R. Soloman, Shlomo S. Sawilowsky

Journal of Modern Applied Statistical Methods

The purpose of this article is to provide an empirical comparison of rank-based normalization methods for standardized test scores. A series of Monte Carlo simulations were performed to compare the Blom, Tukey, Van der Waerden and Rankit approximations in terms of achieving the T score’s specified mean and standard deviation and unit normal skewness and kurtosis. All four normalization methods were accurate on the mean but were variably inaccurate on the standard deviation. Overall, deviation from the target moments was pronounced for the even moments but slight for the odd moments. Rankit emerged as the most accurate method among all …

Bayesian Analysis Of Evidence From Studies Of Warfarin V Aspirin For Symptomatic Intracranial Stenosis, Vicki Hertzberg, Barney Stern, Karen Johnston

Journal of Modern Applied Statistical Methods

Bayesian analyses of symptomatic intracranial stenosis studies were conducted to compare the benefits of long-term therapy with warfarin to aspirin. The synthesis of evidence of effect from previous nonrandomized studies in monitoring a randomized clinical trial was of particular interest. Sequential Bayesian learning analysis was conducted and Bayesian hierarchical random effects models were used to incorporate variability between studies. The posterior point estimates for the risk rate ratio (RRR) were similar between analyses, although the interval estimates resulting from the hierarchical analyses are larger than the corresponding Bayesian learning analyses. This demonstrated the difference between these methods in accounting for …

A Maximum Test For The Analysis Of Ordered Categorical Data, Markus Neuhäeuser

Journal of Modern Applied Statistical Methods

Different scoring schemes are possible when performing exact tests using scores on ordered categorical data. The standard scheme is based on integer scores, but non-integer scores were proposed to increase power (Ivanova & Berger, 2001). However, different non-integer scores exist and the question arises as to which of the non-integer schemes should be chosen. To solve this problem, a maximum test is proposed. To be precise, the maximum of the competing statistics is used as the new test statistic, rather than arbitrarily choosing one single test statistic.

Intermediate R Values For Use In The Fleishman Power Method, Julie M. Smith

Journal of Modern Applied Statistical Methods

Several intermediate r values are calculated at three different correlations for use in the Fleishman Power Method for generating correlated data from normal and non-normal populations.

The Regular Excluded Minors For Signed-Graphic Matroids, Hongxun Qin, Dan Slilaty, Xiangqian Zhou

Mathematics and Statistics Faculty Publications

We show that the complete list of regular excluded minors for the class of signed-graphic matroids is M*(G₁),...,M*(G₂₉),R₁₅,R₁₆. Here G₁,...,G₂₉ are the vertically 2-connected excluded minors for the class of projective-planar graphs and R₁₅ and R₁₆ are two regular matroids that we will define in the article.

Sequence Comparison And Stochastic Model Based On Multi-Order Markov Models, Xiang Fang

Department of Statistics: Dissertations, Theses, and Student Research

This dissertation presents two statistical methodologies developed on multi-order Markov models. First, we introduce an alignment-free sequence comparison method, which represents a sequence using a multi-order transition matrix (MTM). The MTM contains information of multi-order dependencies and provides a comprehensive representation of the heterogeneous composition within a sequence. Based on the MTM, a distance measure is developed for pair-wise comparison of sequences. The new method is compared with the traditional maximum likelihood (ML) method, the complete composition vector (CCV) method and the improved version of the complete composition vector (ICCV) method using simulated sequences. We further illustrate the application of …

Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu

Research Collection School Of Economics

In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact ¯nite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion …

The Em Algorithm For Group Testing Regression Models Under Matrix Pooling, Christopher R. Bilder, Boan Zhang

Department of Statistics: Faculty Publications

No abstract provided.

Observer Reliability Of Cephalometric Landmark Identification On 3-D Mr Images, Michael S. Pollack

Loma Linda University Electronic Theses, Dissertations & Projects

Introduction: Cephalometric analysis is a cornerstone of orthodontic diagnosis, yet much information is lost when 3-dimensional structures are assessed with 2-dimensional methods. CBCT offers 3-dimensional imaging but at the cost of higher levels of ionizing radiation. Magnetic resonance imaging provides 3-dimensional imaging without ionizing radiation and additionally imparts visualization of soft tissue structures. Prior to employing MR images for cephalometric analysis, reliability of landmark identification must be assessed. The purpose of this study was to evaluate intra- and interobserver reliability in 3-dimensional landmark identification using MR images.

Materials and Methods: Fifteen cranial MR images (3.0 T, MP-RAGE) from subjects between …

On The Blue Of The Population Mean For Location And Scale Parameters Of Distributions Based On Moving Extreme Ranked Set Sampling, Walid A. Abu-Dayyeh, Lana Al-Rousan

Journal of Modern Applied Statistical Methods

The best linear unbiased estimator (BLUE) for the population mean under moving extreme ranked set sampling (MERSS) is derived for general location and scale parameters of distributions which generalizes Al-Odat and Al-Saleh (2001). It is compared with the sample mean of simple random sampling (SRS). The efficient sample size under the MERSS for which the BLUE estimator dominates the usual sample mean under SRS for estimating the population mean is also computed for several distributions.

Robustness To Non-Independence And Power Of The I Test For Trend In Construct Validity, John L. Cuzzocrea, Shlomo Sawilowsky

Journal of Modern Applied Statistical Methods

The Multitrait-Multimethod Matrix is used to evaluate construct validity; Sawilowsky (2002) created the I test to analyze the matrix. This article examined the robustness and power of the Sawilowsky I test. Ad hoc critical values were determined to improve the statistical power of the technique for analyzing the Multitrait-Multimethod Matrix.

Least Absolute Value Vs. Least Squares Estimation And Inference Procedures In Regression Models With Asymmetric Error Distributions, Terry E. Dielman

Journal of Modern Applied Statistical Methods

A Monte Carlo simulation is used to compare estimation and inference procedures in least absolute value (LAV) and least squares (LS) regression models with asymmetric error distributions. Mean square errors (MSE) of coefficient estimates are used to assess the relative efficiency of the estimators. Hypothesis tests for coefficients are compared on the basis of empirical level of significance and power.

Covariate-Adjusted Constrained Bayes Predictions Of Random Intercepts And Slopes. Sujit Ghosh Is A, Robert H. Lyles, Reneé H. Moore, Amita K. Manatunga, Kirk A. Easley

Journal of Modern Applied Statistical Methods

No abstract provided.

On The Expected Values Of Distribution Of The Sample Range Of Order Statistics From The Geometric Distribution, Sinan Calik, Cemil Colak, Ayse Turan

Journal of Modern Applied Statistical Methods

The expected values of the distribution of the sample range of order statistics from the geometric distribution are presented. For n up to 10, algebraic expressions for the expected values are obtained. Using the algebraic expressions, expected values based on the p and n values can be easily computed.

Approximations To Power When Comparing Two Small Independent Proportions, Michael Vorburger, Breda Munoz

Journal of Modern Applied Statistical Methods

No abstract provided.

The Bootstrap Method For The Selection Of A Shrinkage Factor In Two-Stage Estimation Of The Reliability Function Of An Exponential Distribution, Makarand V. Ratnaparkhi, Vasant B. Waikar, Fredrick J. Schuurmann

Journal of Modern Applied Statistical Methods

An application of a bootstrap method for selecting a suitable shrinkage factor for the two-stage shrinkage estimator of a reliability function for the exponential distribution is discussed. The estimator obtained here has higher efficiency as compared to the one where the shrinkage factor is not subjected to bootstrapping.

Effects Of Population Distribution, Sample Size And Correlation Structure On Huberty’S Effect Size R, James B. Hittner

Journal of Modern Applied Statistical Methods

Huberty’s (1994) R² is derived by subtracting the expected value of R² from an adjusted R², and the square root of Huberty’s R² is Huberty’s effect size R. The present study examined the effects of population distribution, sample size and population correlation structure on the statistical power of Huberty’s R.