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Articles 31 - 60 of 93
Full-Text Articles in Statistical Theory
Jmasm28: Gibbs Sampling For 2pno Multi-Unidimensional Item Response Theory Models (Fortran), Yanyan Sheng, Todd C. Headrick
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
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
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
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
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
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.
Sequence Comparison And Stochastic Model Based On Multi-Order Markov Models, Xiang Fang
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 …
Quasi-Least Squares With Mixed Linear Correlation Structures, Jichun Xie, Justine Shults, Jon Peet, Dwight Stambolian, Mary F. Cotch
Quasi-Least Squares With Mixed Linear Correlation Structures, Jichun Xie, Justine Shults, Jon Peet, Dwight Stambolian, Mary F. Cotch
UPenn Biostatistics Working Papers
Quasi-least squares (QLS) is a two-stage computational approach for estimation of the correlation parameters in the framework of generalized estimating equations (GEE). We prove two general results for the class of mixed linear correlation structures: namely, that the stage one QLS estimate of the correlation parameter always exists and is feasible (yields a positive definite estimated correlation matrix) for any correlation structure, while the stage two estimator exists and is unique (and therefore consistent) with probability one, for the class of mixed linear correlation structures. Our general results justify the implementation of QLS for particular members of the class of …
Readings In Targeted Maximum Likelihood Estimation, Mark J. Van Der Laan, Sherri Rose, Susan Gruber
Readings In Targeted Maximum Likelihood Estimation, Mark J. Van Der Laan, Sherri Rose, Susan Gruber
U.C. Berkeley Division of Biostatistics Working Paper Series
This is a compilation of current and past work on targeted maximum likelihood estimation. It features the original targeted maximum likelihood learning paper as well as chapters on super (machine) learning using cross validation, randomized controlled trials, realistic individualized treatment rules in observational studies, biomarker discovery, case-control studies, and time-to-event outcomes with censored data, among others. We hope this collection is helpful to the interested reader and stimulates additional research in this important area.
Causal Inference For Nested Case-Control Studies Using Targeted Maximum Likelihood Estimation, Sherri Rose, Mark J. Van Der Laan
Causal Inference For Nested Case-Control Studies Using Targeted Maximum Likelihood Estimation, Sherri Rose, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
A nested case-control study is conducted within a well-defined cohort arising out of a population of interest. This design is often used in epidemiology to reduce the costs associated with collecting data on the full cohort; however, the case control sample within the cohort is a biased sample. Methods for analyzing case-control studies have largely focused on logistic regression models that provide conditional and not marginal causal estimates of the odds ratio. We previously developed a Case-Control Weighted Targeted Maximum Likelihood Estimation (TMLE) procedure for case-control study designs, which relies on the prevalence probability q0. We propose the use of …
Targeted Maximum Likelihood Estimation: A Gentle Introduction, Susan Gruber, Mark J. Van Der Laan
Targeted Maximum Likelihood Estimation: A Gentle Introduction, Susan Gruber, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
This paper provides a concise introduction to targeted maximum likelihood estimation (TMLE) of causal effect parameters. The interested analyst should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. A program written in R is provided. This program implements a basic version of TMLE that can be used to estimate the effect of a binary point treatment on a continuous or binary outcome.
Comparing Risk Scoring Systems Beyond The Roc Paradigm In Survival Analysis, Hajime Uno, Lu Tian, Tianxi Cai, Isaac S. Kohane, L. J. Wei
Comparing Risk Scoring Systems Beyond The Roc Paradigm In Survival Analysis, Hajime Uno, Lu Tian, Tianxi Cai, Isaac S. Kohane, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Combinational Mixtures Of Multiparameter Distributions, Valeria Edefonti, Giovanni Parmigiani
Combinational Mixtures Of Multiparameter Distributions, Valeria Edefonti, Giovanni Parmigiani
Johns Hopkins University, Dept. of Biostatistics Working Papers
We introduce combinatorial mixtures - a flexible class of models for inference on mixture distributions whose component have multidimensional parameters. The key idea is to allow each element of the component-specific parameter vectors to be shared by a subset of other components. This approach allows for mixtures that range from very flexible to very parsimonious, and unifies inference on component-specific parameters with inference on the number of components. We develop Bayesian inference and computation approaches for this class of distributions, and illustrate them in an application. This work was originally motivated by the analysis of cancer subtypes: in terms of …
Shrinkage Estimation Of Expression Fold Change As An Alternative To Testing Hypotheses Of Equivalent Expression, Zahra Montazeri, Corey M. Yanofsky, David R. Bickel
Shrinkage Estimation Of Expression Fold Change As An Alternative To Testing Hypotheses Of Equivalent Expression, Zahra Montazeri, Corey M. Yanofsky, David R. Bickel
COBRA Preprint Series
Research on analyzing microarray data has focused on the problem of identifying differentially expressed genes to the neglect of the problem of how to integrate evidence that a gene is differentially expressed with information on the extent of its differential expression. Consequently, researchers currently prioritize genes for further study either on the basis of volcano plots or, more commonly, according to simple estimates of the fold change after filtering the genes with an arbitrary statistical significance threshold. While the subjective and informal nature of the former practice precludes quantification of its reliability, the latter practice is equivalent to using a …
The Effect Of Correlation In False Discovery Rate Estimation, Armin Schwartzman, Xihong Lin
The Effect Of Correlation In False Discovery Rate Estimation, Armin Schwartzman, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Marginalized Frailty Models For Multivariate Survival Data, Megan Othus, Yi Li
Marginalized Frailty Models For Multivariate Survival Data, Megan Othus, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Harvard University Biostatistics Working Paper Series
No abstract provided.
On The C-Statistics For Evaluating Overall Adequacy Of Risk Prediction Procedures With Censored Survival Data, Hajime Uno, Tianxi Cai, Michael J. Pencina, Ralph B. D'Agostino, L. J. Wei
On The C-Statistics For Evaluating Overall Adequacy Of Risk Prediction Procedures With Censored Survival Data, Hajime Uno, Tianxi Cai, Michael J. Pencina, Ralph B. D'Agostino, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimating Subject-Specific Dependent Competing Risk Profile With Censored Event Time Observations, Yi Li, Lu Tian, L. J. Wei
Estimating Subject-Specific Dependent Competing Risk Profile With Censored Event Time Observations, Yi Li, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
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
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
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
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
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
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
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
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
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.
Estimating Task Duration In Pert Using The Weibull Probability Distribution, Edward L. Mccombs, Matthew E. Elam, David B. Pratt
Estimating Task Duration In Pert Using The Weibull Probability Distribution, Edward L. Mccombs, Matthew E. Elam, David B. Pratt
Journal of Modern Applied Statistical Methods
The Weibull probability distribution can be used as an alternative model for task time estimates in the PERT estimating methodology. It has the same advantages as the traditional beta distribution for this application. It has additional benefits, however, that make it a preferred option.
Beyond Kappa: Estimating Inter-Rater Agreement With Nominal Classifications, Nol Bendermacher, Pierre Souren
Beyond Kappa: Estimating Inter-Rater Agreement With Nominal Classifications, Nol Bendermacher, Pierre Souren
Journal of Modern Applied Statistical Methods
Cohen’s Kappa and a number of related measures can all be criticized for their definition of correction for chance agreement. A measure is introduced that derives the corrected proportion of agreement directly from the data, thereby overcoming objections to Kappa and its related measures.