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Articles 31 - 60 of 96
Full-Text Articles in Statistical Theory
Indeterminacy Of Factor Score Estimates In Slightly Misspecified Confirmatory Factor Models, André Beauducel
Indeterminacy Of Factor Score Estimates In Slightly Misspecified Confirmatory Factor Models, André Beauducel
Journal of Modern Applied Statistical Methods
Two methods to calculate a measure for the quality of factor score estimates have been proposed. These methods were compared by means of a simulation study. The method based on a covariance matrix reproduced from a model leads to smaller effects of sampling error.
Error Analysis On The Generalized Negative Binomial Distribution, Felix Famoye, Oluwakemi Aremu
Error Analysis On The Generalized Negative Binomial Distribution, Felix Famoye, Oluwakemi Aremu
Journal of Modern Applied Statistical Methods
The generalized negative binomial distribution characterized by three parameters, has been used to fit data from various fields of study. The distribution can model data for which the variance is larger or smaller than the mean, however, it becomes truncated under certain conditions. This truncation error is investigated via a detailed error analysis that determines the parameter space when the model can be used in place of the truncated generalized negative binomial distribution. The fitting of a generalized negative. K. M. Ramachandran is a
A Permutation Test For Compound Symmetry With Application To Gene Expression Data, Tracy L. Morris, Mark E. Payton, Stephanie A. Santorico
A Permutation Test For Compound Symmetry With Application To Gene Expression Data, Tracy L. Morris, Mark E. Payton, Stephanie A. Santorico
Journal of Modern Applied Statistical Methods
The development and application of a permutation test for compound symmetry is described. In a simulation study the permutation test appears to be a level-α test and is robust to non-normality. However, it exhibits poor power, particularly for small samples.
Ordinal Regression Analysis: Predicting Mathematics Proficiency Using The Continuation Ratio Model, Xing Liu, Ann A. O'Connell, Hari Koirala
Ordinal Regression Analysis: Predicting Mathematics Proficiency Using The Continuation Ratio Model, Xing Liu, Ann A. O'Connell, Hari Koirala
Journal of Modern Applied Statistical Methods
One commonly used model to analyze ordinal response data is the proportional odds (PO) model. However, if research interest is focused on a particular category and if an individual must pass through lower categories before achieving a higher level, the continuation ratio (CR) model is a more appropriate choice than the PO model. In addition, statistical software, such as Stata and SAS, may use different techniques to estimate the parameters. The CR model is used to illustrate the analysis of ordinal data in education using Stata and SAS and compares the results of fitting the CR model between these two …
Estimation And Hypothesis Testing In Lav Regression With Autocorrelated Errors: Is Correction For Autocorrelation Helpful?, Terry E. Dielman
Estimation And Hypothesis Testing In Lav Regression With Autocorrelated Errors: Is Correction For Autocorrelation Helpful?, Terry E. Dielman
Journal of Modern Applied Statistical Methods
Using the Prais-Winsten correction and adding a lagged variable provides improved estimates (smaller MSE) in least absolute value (LAV) regression when moderate to high levels of autocorrelation are present. When comparing empirical levels of significance for hypothesis tests, adding a lagged variable outperforms other approaches but has a relative high empirical level of significance.
Control Balanced Designs Involving Sequences Of Treatments, Cini Varghese, Seema Jaggi
Control Balanced Designs Involving Sequences Of Treatments, Cini Varghese, Seema Jaggi
Journal of Modern Applied Statistical Methods
Designs involving sequences of treatments for test vs. control comparisons are suitable for research in which each experimental unit receives treatments over time in order to compare several test treatments to one (or more) control treatment(s). These designs can be advantageously used in screening experiments and bioequivalence trials. Three series of such designs are constructed in incomplete sequences wherein the first class of designs is variance balanced while the other two classes of designs are partially variance balanced for test versus test comparisons of both direct and residual effects of treatments.
Probabilistic Inferences For The Sample Pearson Product Moment Correlation, Jeffrey R. Harring, John A. Wasko
Probabilistic Inferences For The Sample Pearson Product Moment Correlation, Jeffrey R. Harring, John A. Wasko
Journal of Modern Applied Statistical Methods
Fisher’s correlation transformation is commonly used to draw inferences regarding the reliability of tests comprised of dichotomous or polytomous items. It is illustrated theoretically and empirically that omitting test length and difficulty results in inflated Type I error. An empirically unbiased correction is introduced within the transformation that is applicable under any test conditions.
Non-Homogenous Poisson Process For Evaluating Stage I & Ii Ductal Breast Cancer Treatment, Chris P. Tsokos, Yong Xu
Non-Homogenous Poisson Process For Evaluating Stage I & Ii Ductal Breast Cancer Treatment, Chris P. Tsokos, Yong Xu
Journal of Modern Applied Statistical Methods
Non-Homogenous Poisson Process (NHPP), also known as the Power Law process (PLP) or the Weibull Process, is used to evaluate the effectiveness of a given treatment for Stage I & II ductal breast cancer patients. The behavior of the shape parameter of the intensity function is examined to evaluate the response of a given treatment with respect to its effectiveness for a cancer subject.
On Maximum Likelihood Estimators Of The Parameters Of A Modified Weibull Distribution Using Extreme Ranked Set Sampling, Amer Ibrahim Al-Omari, Said Ali Al-Hadhrami
On Maximum Likelihood Estimators Of The Parameters Of A Modified Weibull Distribution Using Extreme Ranked Set Sampling, Amer Ibrahim Al-Omari, Said Ali Al-Hadhrami
Journal of Modern Applied Statistical Methods
Extreme ranked set sampling (ERSS) is considered to estimate the three parameters and population mean of the modified Weibull distribution (MWD). The maximum likelihood estimator (MLE) is investigated and compared to the corresponding one based on simple random sampling (SRS). It is found that, the MLE based on ERSS is more efficient than MLE using SRS for estimating the three parameters of the MWD. The ERSS estimator of the population mean of the MWD is also found to be more efficient than the SRS based on the same number of measured units.
Identifying Outliers In Fuzzy Time Series, S. Suresh, K. Senthamarai Kannan
Identifying Outliers In Fuzzy Time Series, S. Suresh, K. Senthamarai Kannan
Journal of Modern Applied Statistical Methods
Time series analysis is often associated with the discovery of patterns and prediction of features. Forecasting accuracy can be improved by removing identified outliers in the data set using the Cook’s distance and Studentized residual test. In this paper a modified fuzzy time series method is proposed based on transition probability vector membership function. It is experimentally shown that the proposed method minimizes the average forecasting error compared with other known existing methods.
Modeling Repairable System Failures With Interval Failure Data And Time Dependent Covariate, Jayanthi Arasan, Samira Ehsani
Modeling Repairable System Failures With Interval Failure Data And Time Dependent Covariate, Jayanthi Arasan, Samira Ehsani
Journal of Modern Applied Statistical Methods
An application of a repairable system model for interval failure data with a time dependent covariate is examined. The performance of several models based on the NHPP when applied to real data on ball bearing failures is also explored. The best model for the data was selected based on results of the likelihood ratio test. The bootstrapping technique was applied to obtain the variance estimate for the estimated expected number of failures. Results demonstrate that the proposed model works well and is easy to implement, in addition the bootstrap variance estimate provides a simple substitute for the traditional estimate.
Salary Equity Studies: An Analysis Of Using The Blinder-Oaxaca Decomposition To Estimate Differences In Faculty Salaries By Gender, Sally A. Lesik, Carolyn R. Fallahi
Salary Equity Studies: An Analysis Of Using The Blinder-Oaxaca Decomposition To Estimate Differences In Faculty Salaries By Gender, Sally A. Lesik, Carolyn R. Fallahi
Journal of Modern Applied Statistical Methods
Parameter estimates for equity studies tested for stability are described. Bootstrap simulation can test whether parameter estimates remain stable given changes in the sample data; fractional polynomials can be used to access functional form specification; and variance inflation factors can be used to test for multicollinearity.
Higher Order C(T, P, S) Crossover Designs, James F. Reed Iii
Higher Order C(T, P, S) Crossover Designs, James F. Reed Iii
Journal of Modern Applied Statistical Methods
A crossover study is a repeated measures design in which each subject is randomly assigned to a sequence of treatments, including at least two treatments. The most damning characteristic of a crossover study is the potential of a carryover effect of one treatment to the next period. To solve the first-order crossover problem characteristic in the classic AB|BA design, the design must be extended. One alternative uses additional treatment sequences in two periods; a second option is to add a third period and repeat one of the treatments. Assuming a traditional model that specifies a first-order carryover effect, this study …
Estimation Of A Non-Parametric Variable Importance Measure Of A Continuous Exposure, Chambaz Antoine, Pierre Neuvial, Mark J. Van Der Laan
Estimation Of A Non-Parametric Variable Importance Measure Of A Continuous Exposure, Chambaz Antoine, Pierre Neuvial, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We define a new measure of variable importance of an exposure on a continuous outcome, accounting for potential confounders. The exposure features a reference level x0 with positive mass and a continuum of other levels. For the purpose of estimating it, we fully develop the semi-parametric estimation methodology called targeted minimum loss estimation methodology (TMLE) [van der Laan & Rubin, 2006; van der Laan & Rose, 2011]. We cover the whole spectrum of its theoretical study (convergence of the iterative procedure which is at the core of the TMLE methodology; consistency and asymptotic normality of the estimator), practical implementation, simulation …
A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error, David M. Zucker, Malka Gorfine, Yi Li, Donna Spiegelman
A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error, David M. Zucker, Malka Gorfine, Yi Li, Donna Spiegelman
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Proof Of Bell's Inequality In Quantum Mechanics Using Causal Interactions, James M. Robins, Tyler J. Vanderweele, Richard D. Gill
A Proof Of Bell's Inequality In Quantum Mechanics Using Causal Interactions, James M. Robins, Tyler J. Vanderweele, Richard D. Gill
COBRA Preprint Series
We give a simple proof of Bell's inequality in quantum mechanics which, in conjunction with experiments, demonstrates that the local hidden variables assumption is false. The proof sheds light on relationships between the notion of causal interaction and interference between particles.
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Harvard University Biostatistics Working Paper Series
When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. …
Multiple Testing Of Local Maxima For Detection Of Peaks In Chip-Seq Data, Armin Schwartzman, Andrew Jaffe, Yulia Gavrilov, Clifford A. Meyer
Multiple Testing Of Local Maxima For Detection Of Peaks In Chip-Seq Data, Armin Schwartzman, Andrew Jaffe, Yulia Gavrilov, Clifford A. Meyer
Harvard University Biostatistics Working Paper Series
No abstract provided.
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Variable Importance Analysis With The Multipim R Package, Stephan J. Ritter, Nicholas P. Jewell, Alan E. Hubbard
Variable Importance Analysis With The Multipim R Package, Stephan J. Ritter, Nicholas P. Jewell, Alan E. Hubbard
U.C. Berkeley Division of Biostatistics Working Paper Series
We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE) of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including …
A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi
A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi
COBRA Preprint Series
Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The nonnegativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. In the last few years, NMF has been successfully applied in a variety of areas such as natural language processing, information retrieval, image processing, speech recognition …
Multiple Testing Of Local Maxima For Detection Of Unimodal Peaks In 1d, Armin Schwartzman, Yulia Gavrilov, Robert J. Adler
Multiple Testing Of Local Maxima For Detection Of Unimodal Peaks In 1d, Armin Schwartzman, Yulia Gavrilov, Robert J. Adler
Harvard University Biostatistics Working Paper Series
No abstract provided.
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale
Johns Hopkins University, Dept. of Biostatistics Working Papers
Biomedical signals can arise from one or many sources including heart ,brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal are like electroencephalogram(EEG),electrocardiogram(ECG),etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The diagnosis of patient is based on visual observation of recorded ECG,EEG,etc, may not be accurate. To achieve better understanding , PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals . The immense scope in the field of biomedical-signal processing Independent Component Analysis( …
Propensity Score Analysis With Matching Weights, Liang Li
Propensity Score Analysis With Matching Weights, Liang Li
COBRA Preprint Series
The propensity score analysis is one of the most widely used methods for studying the causal treatment effect in observational studies. This paper studies treatment effect estimation with the method of matching weights. This method resembles propensity score matching but offers a number of new features including efficient estimation, rigorous variance calculation, simple asymptotics, statistical tests of balance, clearly identified target population with optimal sampling property, and no need for choosing matching algorithm and caliper size. In addition, we propose the mirror histogram as a useful tool for graphically displaying balance. The method also shares some features of the inverse …
Model Diagnostics For Proportional And Partial Proportional Odds Models, Ann A. O'Connell, Xing Liu
Model Diagnostics For Proportional And Partial Proportional Odds Models, Ann A. O'Connell, Xing Liu
Journal of Modern Applied Statistical Methods
Although widely used to assist in evaluating the prediction quality of linear and logistic regression models, residual diagnostic techniques are not well developed for regression analyses where the outcome is treated as ordinal. The purpose of this article is to review methods of model diagnosis that may be useful in investigating model assumptions and in identifying unusual cases for PO and PPO models, and provide a corresponding application of these diagnostic methods to the prediction of proficiency in early literacy for children drawn from the kindergarten cohort of the Early Childhood Longitudinal Study (ECLS-K; NCES, 2000).
Sample Size Considerations For Multiple Comparison Procedures In Anova, Gordon P. Brooks, George A. Johanson
Sample Size Considerations For Multiple Comparison Procedures In Anova, Gordon P. Brooks, George A. Johanson
Journal of Modern Applied Statistical Methods
Adequate sample sizes for omnibus ANOVA tests do not necessarily provide sufficient statistical power for post hoc multiple comparisons typically performed following a significant omnibus F test. Results reported support a comparison-of-most-interest approach for sample size determination in ANOVA based on effect sizes for multiple comparisons.
Type I Error Inflation Of The Separate-Variances Welch T Test With Very Small Sample Sizes When Assumptions Are Met, Albert K. Adusah, Gordon P. Brooks
Type I Error Inflation Of The Separate-Variances Welch T Test With Very Small Sample Sizes When Assumptions Are Met, Albert K. Adusah, Gordon P. Brooks
Journal of Modern Applied Statistical Methods
This Monte Carlo study shows that the separate-variances Welch t test has inflated Type I error rates at very small sample sizes, especially when sample sizes are very small in one group and larger in the second group – even when all assumptions for the statistical test are met.
One Is Not Enough: The Need For Multiple Respondents In Survey Research Of Organizations, Joseph L. Balloun, Hilton Barrett, Art Weinstein
One Is Not Enough: The Need For Multiple Respondents In Survey Research Of Organizations, Joseph L. Balloun, Hilton Barrett, Art Weinstein
Journal of Modern Applied Statistical Methods
The need for multiple respondents per organization in organizational survey research is supported. Leadership teams’ ratings of their implementations of market orientation are examined, along with learning orientation, entrepreneurial management, and organizational flexibility. Sixty diverse organizations, including not-for-profit organizations in education and healthcare as well as manufacturing and service businesses, were included. The major finding was the large rating variance within the leadership teams of each organization. The results are enlightening and have definite implications for improved design of survey research on organizations.
Is Next Twelve Months Period Tumor Recurrence Free Under Restricted Rate Due To Medication? A Probabilistic Warning, Ramalingam Shanmugam
Is Next Twelve Months Period Tumor Recurrence Free Under Restricted Rate Due To Medication? A Probabilistic Warning, Ramalingam Shanmugam
Journal of Modern Applied Statistical Methods
A methodology is formulated to analyze tumor recurrence data when its incidence rate is restricted due to medication. Analytic results are derived to make a probabilistic early warning of tumor recurrence free period of length τ; that is, the chance for a safe period of lengthτ is estimated. The captured data are length biased. Expressions are developed to extract and relate to counterparts of the non-length biased data. Three data sets are considered as illustrations: (1) patients who are given a placebo, (2) patients who are given the medicine pyridoxine and (3) patients who are given the medicine thiotepa.
The Likelihood Of Choosing The Borda-Winner With Partial Preference Rankings Of The Electorate, Ömer Eğecioğlu, Ayça Ebru Giritligil
The Likelihood Of Choosing The Borda-Winner With Partial Preference Rankings Of The Electorate, Ömer Eğecioğlu, Ayça Ebru Giritligil
Journal of Modern Applied Statistical Methods
Given that n voters report only the first r (1 ≤ r < m) ranks of their linear preference rankings over m alternatives, the likelihood of implementing Borda outcome is investigated. The information contained in the first r ranks is aggregated through a Borda-like method, namely the r-Borda rule. Monte-Carlo simulations are run to detect changes in the likelihood of r-Borda winner(s) to coincide with the original Borda winner(s) as a function of m, n and r. The voters’ preferences are generated through the Impartial Anonymous and Neutral Culture Model, where both the names of the …