Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Wayne State University (1088)
- Central Bank of Nigeria (26)
- University of Denver (4)
- City University of New York (CUNY) (1)
- Claremont Colleges (1)
-
- Georgetown University Law Center (1)
- Kennesaw State University (1)
- Old Dominion University (1)
- Southern Methodist University (1)
- The Texas Medical Center Library (1)
- Universitas Indonesia (1)
- University of Arkansas, Fayetteville (1)
- University of Connecticut (1)
- University of Nebraska - Lincoln (1)
- University of Nebraska at Omaha (1)
- Ursinus College (1)
- Virginia Commonwealth University (1)
- Western Kentucky University (1)
- Keyword
-
- Bias (29)
- Monte Carlo simulation (27)
- Bootstrap (26)
- Simulation (26)
- Power (23)
-
- Confidence interval (22)
- Mean squared error (20)
- Effect size (19)
- Type I error (19)
- Monte Carlo (18)
- Robustness (18)
- Sample size (18)
- Multicollinearity (17)
- Permutation test (17)
- Maximum likelihood estimation (16)
- Missing data (16)
- Reliability (14)
- Confidence intervals (13)
- Efficiency (13)
- Heteroscedasticity (13)
- Logistic regression (13)
- SPSS (13)
- P-value (12)
- Simple random sampling (12)
- Estimation (11)
- Mean square error (11)
- Meta-analysis (11)
- Nonnormality (11)
- ANOVA (10)
- Bayesian (10)
- Publication Year
- Publication
-
- Journal of Modern Applied Statistical Methods (1088)
- CBN Journal of Applied Statistics (JAS) (26)
- Electronic Theses and Dissertations (2)
- Human Rights & Human Welfare (2)
- Business and Economics Honors Papers (1)
-
- CHIP Documents (1)
- Department of Management: Faculty Publications (1)
- Dissertations (1)
- Dissertations and Theses (Open Access) (1)
- Dissertations, Theses, and Capstone Projects (1)
- Georgetown Law Faculty Publications and Other Works (1)
- Information Systems Undergraduate Honors Theses (1)
- Jurnal Administrasi Bisnis Terapan (1)
- Masters Theses & Specialist Projects (1)
- Pomona Senior Theses (1)
- Publications (1)
- Statistical Science Theses and Dissertations (1)
- Theses and Dissertations (1)
- VMASC Publications (1)
- Publication Type
Articles 211 - 240 of 1133
Full-Text Articles in Statistical Theory
Stochastic Model For Cancer Cell Growth Through Single Forward Mutation, Jayabharathiraj Jayabalan
Stochastic Model For Cancer Cell Growth Through Single Forward Mutation, Jayabharathiraj Jayabalan
Journal of Modern Applied Statistical Methods
A stochastic model for cancer cell growth in any organ is presented, based on a single forward mutation. Cell growth is explained in a one-dimensional stochastic model, and statistical measures for the variable representing the number of malignant cells are derived. A numerical study is conducted to observe the behavior of the model.
Genetic Algorithms For Cross-Calibration Of Categorical Data, Suja M. Aboukhamseen, Rym A. M'Hallah
Genetic Algorithms For Cross-Calibration Of Categorical Data, Suja M. Aboukhamseen, Rym A. M'Hallah
Journal of Modern Applied Statistical Methods
The probabilistic problem of cross-calibration of two categorical variables is addressed. A probabilistic forecast of the categorical variables is obtained based on a sample of observed data. This forecast is the output of a genetic algorithm based approach, which makes no assumption on the type of relationship between the two variables and applies a scoring rule to assess the fitness of the chromosomes. It converges to a good-quality point probability forecast of the joint distribution of the two variables. The proposed approach is applied both at stationary points in time and across time. Its performance is enhanced when additional sampled …
A Schmid-Leiman-Based Transformation Resulting In Perfect Inter-Correlations Of Three Types Of Factor Score Predictors, André Beauducel
A Schmid-Leiman-Based Transformation Resulting In Perfect Inter-Correlations Of Three Types Of Factor Score Predictors, André Beauducel
Journal of Modern Applied Statistical Methods
Factor score predictors are computed when individual factor scores are of interest. Conditions for a perfect inter-correlation of the best linear factor score predictor, the best linear conditionally unbiased predictor, and the determinant best linear correlation-preserving predictor are presented. A transformation resulting in perfect correlations of the three predictors is proposed.
Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane
Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane
Journal of Modern Applied Statistical Methods
An SPSS script previously presented in this journal contained nontrivial flaws. The script should not be used as written. A call is renewed for validation of new software.
An Empirical Comparison Between Robust Estimation And Robust Optimization To Mean-Variance Portfolio, Epha Diana Supandi, Dedi Rosadi, Abdurakhman
An Empirical Comparison Between Robust Estimation And Robust Optimization To Mean-Variance Portfolio, Epha Diana Supandi, Dedi Rosadi, Abdurakhman
Journal of Modern Applied Statistical Methods
Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Recently, there are two approaches designed to reduce the effect of estimation error: robust statistics and robust optimization. Two different robust portfolios were examined by assessing the out-of-sample performance and the stability of optimal portfolio compositions. The performance of the proposed robust portfolios was compared to classical portfolios via expected return, risk, and Sharpe Ratio. The aim is to shed light on the debate concerning the importance of the estimation error and weights stability in the portfolio allocation problem, and …
Guidelines For Generating Right-Censored Outcomes From A Cox Model Extended To Accommodate Time-Varying Covariates, Maria E. Montez-Rath, Kristopher Kapphahn, Maya B. Mathur, Aya A. Mitani, David J. Hendry, Manisha Desai
Guidelines For Generating Right-Censored Outcomes From A Cox Model Extended To Accommodate Time-Varying Covariates, Maria E. Montez-Rath, Kristopher Kapphahn, Maya B. Mathur, Aya A. Mitani, David J. Hendry, Manisha Desai
Journal of Modern Applied Statistical Methods
Simulating studies with right-censored outcomes as functions of time-varying covariates is discussed. Guidelines on the use of an algorithm developed by Zhou and implemented by Hendry are provided. Through simulation studies, the sensitivity of the method to user inputs is considered.
A Reinterpretation And Extension Of Mcnemar’S Test, Chauncey M. Dayton
A Reinterpretation And Extension Of Mcnemar’S Test, Chauncey M. Dayton
Journal of Modern Applied Statistical Methods
The McNemar test is extended to multiple groups based on a latent class model incorporating classes representing consistent responders and a single latent error rate. The method is illustrated with data from a CDC survey of immunizations for flu and pneumonia for which a part-heterogeneous model is selected for interpretation.
In Response To Frane, "Errors In A Program For Approximating Confidence Intervals", David A. Walker
In Response To Frane, "Errors In A Program For Approximating Confidence Intervals", David A. Walker
Journal of Modern Applied Statistical Methods
A rebuttal to Frane's letter to the Editor in this issue.
Experiment-Wise Type I Error Rates In Nested (Hierarchical) Study Designs, Jack Sawilowsky, Barry Markman
Experiment-Wise Type I Error Rates In Nested (Hierarchical) Study Designs, Jack Sawilowsky, Barry Markman
Journal of Modern Applied Statistical Methods
When conducting a statistical test one of the initial risks that must be considered is a Type I error, also known as a false positive. The Type I error rate is set by nominal alpha, assuming all underlying conditions of the statistic are met. Experiment-wise Type I error inflation occurs when multiple tests are conducted overall for a single experiment. There is a growing trend in the social and behavioral sciences utilizing nested designs. A Monte Carlo study was conducted using a two-layer design. Five theoretical distributions and four real datasets taken from Micceri (1989) were used, each with five …
Control Charts For Mean For Non-Normally Correlated Data, J. R. Singh, Ab Latif Dar
Control Charts For Mean For Non-Normally Correlated Data, J. R. Singh, Ab Latif Dar
Journal of Modern Applied Statistical Methods
Traditionally, quality control methodology is based on the assumption that serially-generated data are independent and normally distributed. On the basis of these assumptions the operating characteristic (OC) function of the control chart is derived after setting the control limits. But in practice, many of the basic industrial variables do not satisfy both the assumptions and hence one may doubt the validity of the inferences drawn from the control charts. In this paper the power of the control chart for the mean is examined when both the assumptions of independence and normality are not tenable. The OC function is calculated and …
Multivariate Rank Outlyingness And Correlation Effects, Olusola Samuel Makinde
Multivariate Rank Outlyingness And Correlation Effects, Olusola Samuel Makinde
Journal of Modern Applied Statistical Methods
The effect of correlation on multivariate rank outlyingness, a result of deviation of multivariate rank functions from property of spherical symmetry, is examined. Possible affine invariant versions of this multivariate rank are surveyed, and outlyingness of affine invariant and non-invariant spatial rank functions under general affine transformation are compared.
A Comparison Of Different Methods Of Zero-Inflated Data Analysis And An Application In Health Surveys, Si Yang, Lisa L. Harlow, Gavino Puggioni, Colleen A. Redding
A Comparison Of Different Methods Of Zero-Inflated Data Analysis And An Application In Health Surveys, Si Yang, Lisa L. Harlow, Gavino Puggioni, Colleen A. Redding
Journal of Modern Applied Statistical Methods
The performance of several models under different conditions of zero-inflation and dispersion are evaluated. Results from simulated and real data showed that the zero-altered or zero-inflated negative binomial model were preferred over others (e.g., ordinary least-squares regression with log-transformed outcome, Poisson model) when data have excessive zeros and over-dispersion.
Test Statistics For The Comparison Of Means For Two Samples That Include Both Paired And Independent Observations, Ben Derrick, Bethan Russ, Deirdre Toher, Paul White
Test Statistics For The Comparison Of Means For Two Samples That Include Both Paired And Independent Observations, Ben Derrick, Bethan Russ, Deirdre Toher, Paul White
Journal of Modern Applied Statistical Methods
Standard approaches for analyzing the difference in two means, where partially overlapping samples are present, are less than desirable. Here are introduced two test statistics, making reference to the t-distribution. It is shown that these test statistics are Type I error robust, and more powerful than standard tests.
Graphical Log-Linear Models: Fundamental Concepts And Applications, Niharika Gauraha
Graphical Log-Linear Models: Fundamental Concepts And Applications, Niharika Gauraha
Journal of Modern Applied Statistical Methods
A comprehensive study of graphical log-linear models for contingency tables is presented. High-dimensional contingency tables arise in many areas. Analysis of contingency tables involving several factors or categorical variables is very hard. To determine interactions among various factors, graphical and decomposable log-linear models are preferred. Connections between the conditional independence in probability and graphs are explored, followed with illustrations to describe how graphical log-linear model are useful to interpret the conditional independences between factors. The problem of estimation and model selection in decomposable models is discussed.
Robustness And Power Comparison Of The Mood-Westenberg And Siegel-Tukey Tests, Linda C. Lowenstein, Shlomo S. Sawilowsky
Robustness And Power Comparison Of The Mood-Westenberg And Siegel-Tukey Tests, Linda C. Lowenstein, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
The Mood-Westenberg and Siegel-Tukey tests were examined to determine their robustness with respect to Type-I error for detecting variance changes when their assumptions of equal means were slightly violated, a condition that approaches the Behrens-Fisher problem. Monte Carlo methods were used via 34,606 variations of sample sizes, α levels, distributions/data sets, treatments modeled as a change in scale, and treatments modeled as a shift in means. The Siegel-Tukey was the more robust, and was able to handle a more diverse set of conditions.
A New Estimator For The Pickands Dependence Function, Marta Ferreira
A New Estimator For The Pickands Dependence Function, Marta Ferreira
Journal of Modern Applied Statistical Methods
The Pickands dependence function characterizes an extreme value copula, a useful tool in the modeling of multivariate extremes. A new estimator is presented along with its convergence properties and performance through simulation.
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Journal of Modern Applied Statistical Methods
Estimation of population variance in two-phase (double) sampling is considered using information on multiple auxiliary variables. An unbiased estimator is proposed and its properties are studied under two different structures. The superiority of the suggested estimator over some contemporary estimators of population variance was established through empirical studies from a natural and an artificially generated dataset.
Vol. 16, No. 1 (Full Issue), Jmasm Editors
Vol. 16, No. 1 (Full Issue), Jmasm Editors
Journal of Modern Applied Statistical Methods
.
Robust Ancova: Confidence Intervals That Have Some Specified Simultaneous Probability Coverage When There Is Curvature And Two Covariates, Rand Wilcox
Journal of Modern Applied Statistical Methods
Consider the commonly occurring situation where the goal is to compare two independent groups and there are two covariates. Let Mj(X) be some conditional measure of location for the jth group associated with some random variable Y given X = (X1, X2). The goal is to H0: M1(X) = M2(X) for each X Ω in a manner that controls the probability of one or more Type I errors. An extant technique (method M1 here) addresses this goal without making any parametric assumption about Mj(X). However, a practical concern is that it does not provide enough detail regarding where the regression …
Limitations In The Systematic Analysis Of Structural Equation Model Fit Indices, Sarah A. Rose, Barry Markman, Shlomo Sawilowsky
Limitations In The Systematic Analysis Of Structural Equation Model Fit Indices, Sarah A. Rose, Barry Markman, Shlomo Sawilowsky
Journal of Modern Applied Statistical Methods
The purpose of this study was to evaluate the sensitivity of selected fit index statistics in determining model fit in structural equation modeling (SEM). The results indicated a large dependency on correlation magnitude of the input correlation matrix, with mixed results when the correlation magnitudes were low and a primary indication of good model fit. This was due to the default SEM method of Maximum Likelihood that assumes unstandardized correlation values. However, this warning is not well-known, and is only obscurely mentioned in some textbooks. Many SEM computer software programs do not give appropriate error indications that the results are …
A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, Joghee Ravichandran
A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, Joghee Ravichandran
Journal of Modern Applied Statistical Methods
In most empirical studies (clinical, network modeling, and survey-based and aeronautical studies, etc.), sample observations are drawn from population to analyze and draw inferences about the population. Such analysis is done with reference to a measurable quality characteristic of a product or process of interest. However, fixing a sample size is an important task that has to be decided by the experimenter. One of the means in deciding an appropriate sample size is the fixation of error limit and the associated confidence level. This implies that the analysis based on the sample used must guarantee the prefixed error and confidence …
Methodology For Constructing Perceptual Maps Incorporating Measuring Error In Sensory Acceptance Tests, Elisa Norberto Ferreira Santos, Gilberto Rodrigues Liska, Marcelo Angelo Cirillo
Methodology For Constructing Perceptual Maps Incorporating Measuring Error In Sensory Acceptance Tests, Elisa Norberto Ferreira Santos, Gilberto Rodrigues Liska, Marcelo Angelo Cirillo
Journal of Modern Applied Statistical Methods
A new method is proposed based on construction of perceptual maps using techniques of correspondence analysis and interval algebra that allow specifying the measurement error expected in panel choices in the evaluation form described in unstructured 9-point hedonic scale.
Confidence Intervals For The Scaled Half-Logistic Distribution Under Progressive Type-Ii Censoring, Kiran Ganpati Potdar, D. T. Shirke
Confidence Intervals For The Scaled Half-Logistic Distribution Under Progressive Type-Ii Censoring, Kiran Ganpati Potdar, D. T. Shirke
Journal of Modern Applied Statistical Methods
Confidence interval construction for the scale parameter of the half-logistic distribution is considered using four different methods. The first two are based on the asymptotic distribution of the maximum likelihood estimator (MLE) and log-transformed MLE. The last two are based on pivotal quantity and generalized pivotal quantity, respectively. The MLE for the scale parameter is obtained using the expectation-maximization (EM) algorithm. Performances are compared with the confidence intervals proposed by Balakrishnan and Asgharzadeh via coverage probabilities, length, and coverage-to-length ratio. Simulation results support the efficacy of the proposed approach.
A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, Nilgün Özgül, Hülya Çıngı
A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, Nilgün Özgül, Hülya Çıngı
Journal of Modern Applied Statistical Methods
An exponential-type estimator is developed for the population mean of the sensitive study variable based on various Randomized Response Techniques (RRT) using a non-sensitive auxiliary variable. The mean squared error (MSE) of the proposed estimator is derived for generalized RRT models. The proposed estimator is compared with competitors in a simulation study and an application. The proposed estimator is found to be more efficient using a non-sensitive auxiliary variable.
Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, B. Vijaya Lakshmi, V. Mohan
Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, B. Vijaya Lakshmi, V. Mohan
Journal of Modern Applied Statistical Methods
Shape-based feature extraction in content-based image retrieval is an important research area at present. An algorithm is presented, based on shape features, to enhance the set of features useful in a leaf identification system.
Multiple Ratio Imputation By The Emb Algorithm: Theory And Simulation, Masayoshi Takahashi
Multiple Ratio Imputation By The Emb Algorithm: Theory And Simulation, Masayoshi Takahashi
Journal of Modern Applied Statistical Methods
Although multiple imputation is the gold standard of treating missing data, single ratio imputation is often used in practice. Based on Monte Carlo simulation, the Expectation-Maximization with Bootstrapping (EMB) algorithm to create multiple ratio imputation is used to fill in the gap between theory and practice.
Jmasm45: A Computer Program For Bayesian D-Optimal Binary Repeated Measurements Designs (Matlab), Haftom Temesgen Abebe, Frans E. S. Tan, Gerard J. P. Van Breukelen, Martijn P. F. Berger
Jmasm45: A Computer Program For Bayesian D-Optimal Binary Repeated Measurements Designs (Matlab), Haftom Temesgen Abebe, Frans E. S. Tan, Gerard J. P. Van Breukelen, Martijn P. F. Berger
Journal of Modern Applied Statistical Methods
Planners of longitudinal studies of binary responses in applied sciences have not yet benefitted from optimal designs, which have been shown to improve precision of model parameter estimates, due to absence of a computer program. An interactive computer program for Bayesian optimal binary repeated measurements designs is presented for this purpose.
An Extended Weighted Exponential Distribution, Abbas Mahdavi, Leila Jabari
An Extended Weighted Exponential Distribution, Abbas Mahdavi, Leila Jabari
Journal of Modern Applied Statistical Methods
A new class of weighted distributions is proposed by incorporating an extended exponential distribution in Azzalini’s (1985) method. Several statistics and reliability properties of this new class of distribution are obtained. Maximum likelihood estimators of the unknown parameters cannot be obtained in explicit forms; they have to be obtained by solving some numerical methods. Two data sets are analyzed for illustrative purposes, and show that the proposed model can be used effectively in analyzing real data.
A Comparison Of Depth Functions In Maximal Depth Classification Rules, Olusola Samuel Makinde, Adeyinka Damilare Adewumi
A Comparison Of Depth Functions In Maximal Depth Classification Rules, Olusola Samuel Makinde, Adeyinka Damilare Adewumi
Journal of Modern Applied Statistical Methods
Data depth has been described as alternative to some parametric approaches in analyzing many multivariate data. Many depth functions have emerged over two decades and studied in literature. In this study, a nonparametric approach to classification based on notions of different data depth functions is considered and some properties of these methods are studied. The performance of different depth functions in maximal depth classifiers is investigated using simulation and real data with application to agricultural industry.
The Double Prior Selection For The Parameter Of Exponential Life Time Model Under Type Ii Censoring, Ronak M. Patel, Achyut C. Patel
The Double Prior Selection For The Parameter Of Exponential Life Time Model Under Type Ii Censoring, Ronak M. Patel, Achyut C. Patel
Journal of Modern Applied Statistical Methods
A comparison of double informative priors assumed for the parameter of exponential life time model is considered. Three different sets of double priors are included, and the results are compared with a forth single prior. The data is Type II censored and Bayes estimators for the parameter and reliability are carried out under a squared error loss function in the cases of the four different sets of prior distributions. The predictive distribution was derived for future failure time and also for the remaining ordered failure times after the first r failure times have been observed. Corresponding Bayes credible equal tail …