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Articles 601 - 630 of 1133
Full-Text Articles in Statistical Theory
Information Technology For Increasing Qualitative Information Processing Efficiency, S. N. Martyshenko, E. A. Egorov
Information Technology For Increasing Qualitative Information Processing Efficiency, S. N. Martyshenko, E. A. Egorov
Journal of Modern Applied Statistical Methods
The problem of qualitative information processing in questionnaires is considered and a solution for this problem is offered. The computer technology developed by the authors to automate the offered decision is described.
General Piecewise Growth Mixture Model: Word Recognition Development For Different Learners In Different Phases, Amery D. Wu, Bruno D. Zumbo, Linda S. Siegel
General Piecewise Growth Mixture Model: Word Recognition Development For Different Learners In Different Phases, Amery D. Wu, Bruno D. Zumbo, Linda S. Siegel
Journal of Modern Applied Statistical Methods
The General Piecewise Growth Mixture Model (GPGMM), without losing generality to other fields of study, can answer six crucial research questions regarding children’s word recognition development. Using child word recognition data as an example, this study demonstrates the flexibility and versatility of the GPGMM in investigating growth trajectories that are potentially phasic and heterogeneous. The strengths and limitations of the GPGMM and lessons learned from this hands-on experience are discussed.
A Simulation Study Of The Relative Efficiency Of The Minimized Integrated Square Error Estimator (L2e) For Phase I Control Charting, John N. Dyer
Journal of Modern Applied Statistical Methods
Parameter estimates used in control charting, the sample mean and variance, are based on maximum likelihood estimation (MLE). Unfortunately, MLEs are not robust to contaminated data and can lead to improper conclusions regarding parameter values. This article proposes a more robust estimation technique; the minimized integrated square error estimator (L2E).
Maximum Likelihood Solution For The Linear Structural Relationship With Three Parameters Known, Androulla Michaeloudis
Maximum Likelihood Solution For The Linear Structural Relationship With Three Parameters Known, Androulla Michaeloudis
Journal of Modern Applied Statistical Methods
A maximum likelihood solution is obtained for the simple linear structural relation model where the underlying incidental distribution and one error variance are assumed known. Expressions for the asymptotic standard errors of the maximum likelihood estimates are obtained and these are verified using a simulation study.
Logistic Regression Models For Higher Order Transition Probabilities Of Markov Chain For Analyzing The Occurrences Of Daily Rainfall Data, Narayan Chanra Sinha, M. Ataharul Islam, Kazi Saleh Ahamed
Logistic Regression Models For Higher Order Transition Probabilities Of Markov Chain For Analyzing The Occurrences Of Daily Rainfall Data, Narayan Chanra Sinha, M. Ataharul Islam, Kazi Saleh Ahamed
Journal of Modern Applied Statistical Methods
Logistic regression models for transition probabilities of higher order Markov models are developed for the sequence of chain dependent repeated observations. To identify the significance of these models and their parameters a test procedure for a likelihood ratio criterion is developed. A method of model selection is suggested on the basis of AIC and BIC procedures. The proposed models and test procedures are applied to analyze the occurrences of daily rainfall data for selected stations in Bangladesh. Based on results from these models, the transition probabilities of first order Markov model for temperature and humidity provided the most suitable option …
Number Of Replications Required In Monte Carlo Simulation Studies: A Synthesis Of Four Studies, Daniel J. Mundform, Jay Schaffer, Myoung-Jin Kim, Dale Shaw, Ampai Thongteeraparp, Pornsin Supawan
Number Of Replications Required In Monte Carlo Simulation Studies: A Synthesis Of Four Studies, Daniel J. Mundform, Jay Schaffer, Myoung-Jin Kim, Dale Shaw, Ampai Thongteeraparp, Pornsin Supawan
Journal of Modern Applied Statistical Methods
Monte Carlo simulations are used extensively to study the performance of statistical tests and control charts. Researchers have used various numbers of replications, but rarely provide justification for their choice. Currently, no empirically-based recommendations regarding the required number of replications exist. Twenty-two studies were re-analyzed to determine empirically-based recommendations.
Matched-Pair Studies With Misclassified Ordinal Data, Tze-San Lee
Matched-Pair Studies With Misclassified Ordinal Data, Tze-San Lee
Journal of Modern Applied Statistical Methods
The problem of matched-pair studies with misclassified ordinal data is considered. Misclassification is assumed to occur only between the adjacent columns/rows. Bias-adjusted generalized odds ratio and a test for marginal homogeneity are presented to account for misclassification bias. Data from lambing records of 227 Merino ewes are used to illustrate how to calculate these bias-adjusted estimators and – because validation data are not available – a sensitivity analysis is conducted.
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Journal of Modern Applied Statistical Methods
A robust Root Mean Square Standardized Effect Size (RMSSER) was developed to address the unsatisfactory performance of the Root Mean Square Standardized Effect Size. The coverage performances of the confidence intervals (CI) for RMSSER were investigated. The coverage probabilities of the non-central F distribution-based CI for RMSSER were adequate.
The Overall F-Tests For Seasonal Unit Roots Under Nonstationary Alternatives: Some Theoretical Results And A Monte Carlo Investigation, Ghassen El Montasser
The Overall F-Tests For Seasonal Unit Roots Under Nonstationary Alternatives: Some Theoretical Results And A Monte Carlo Investigation, Ghassen El Montasser
Journal of Modern Applied Statistical Methods
In many empirical studies concerning seasonal time series, it has been shown that the whole set of unit roots associated with seasonal random walks are not present. This article focuses on the overall F-tests for seasonal unit roots under some nonstationary alternatives different from the seasonal random walk. The asymptotic theory of these tests is established for these cases using a new approach based on circulant matrix concepts. The simulation results joined to this theoretic analysis showed that the overall F-tests, as well as their augmented versions, maintained high power against the nonstationary alternatives.
Weighting Large Datasets With Complex Sampling Designs: Choosing The Appropriate Variance Estimation Method, Sara Mann, James Chowhan
Weighting Large Datasets With Complex Sampling Designs: Choosing The Appropriate Variance Estimation Method, Sara Mann, James Chowhan
Journal of Modern Applied Statistical Methods
Using the Canadian Workplace and Employee Survey (WES), three variance estimation methods for weighting large datasets with complex sampling designs are compared: simple final weighting, standard bootstrapping and mean bootstrapping. Using a logit analysis, it is shown - depending on which weighting method is used - different predictor variables are significant. The potential lack of independence inherent in a multi-stage cluster sample design, as in the WES, results in a downward bias in the variance when conducting statistical inference (using the simple final weight), which in turn results in increased Type I errors. Bootstrap methods can account for the survey’s …
Using Finite Mixture Modeling To Deal With Systematic Measurement Error: A Case Study, Min Liu, Gregory R. Hancock, Jeffrey R. Harring
Using Finite Mixture Modeling To Deal With Systematic Measurement Error: A Case Study, Min Liu, Gregory R. Hancock, Jeffrey R. Harring
Journal of Modern Applied Statistical Methods
Conventional methods and analyses view measurement error as random. A scenario is presented where a variable was measured with systematic error. Mixture models with systematic parameter constraints were used to test hypotheses in the context of general linear models; this accommodated the heterogeneity arising due to systematic measurement error.
Estimating Internal Consistency Using Bayesian Methods, Miguel A. Padilla, Guili Zhang
Estimating Internal Consistency Using Bayesian Methods, Miguel A. Padilla, Guili Zhang
Journal of Modern Applied Statistical Methods
Bayesian internal consistency and its Bayesian credible interval (BCI) are developed and Bayesian internal consistency and its percentile and normal theory based BCIs were investigated in a simulation study. Results indicate that the Bayesian internal consistency is relatively unbiased under all investigated conditions and the percentile based BCIs yielded better coverage performance.
Generalized Variances Ratio Test For Comparing K Covariance Matrices From Dependent Normal Populations, Marcelo Angelo Cirillo, Daniel Furtado Ferreira, Thelma Sáfadi, Eric Batista Ferreira
Generalized Variances Ratio Test For Comparing K Covariance Matrices From Dependent Normal Populations, Marcelo Angelo Cirillo, Daniel Furtado Ferreira, Thelma Sáfadi, Eric Batista Ferreira
Journal of Modern Applied Statistical Methods
New tests based on the ratio of generalized variances are presented to compare covariance matrices from dependent normal populations. Monte Carlo simulation concluded that the tests considered controlled the Type I error, providing empirical probabilities that were consistent with the nominal level stipulated.
A Ga-Based Sales Forecasting Model Incorporating Promotion Factors, Li-Chih Wang, Chin-Lien Wang
A Ga-Based Sales Forecasting Model Incorporating Promotion Factors, Li-Chih Wang, Chin-Lien Wang
Journal of Modern Applied Statistical Methods
Because promotions are critical factors highly related to product sales of consumer packaged goods (CPG) companies, predictors concerning sales forecast of CPG products must take promotions into consideration. Decomposition regression incorporating contextual factors offers a method for exploiting both reliability of statistical forecasting and flexibility of judgmental forecasting employing domain knowledge. However, it suffers from collinearity causing poor performance in variable identification and parameter estimation with traditional ordinary least square (OLS). Empirical research evidence shows that - in the case of collinearity - in variable identification, parameter estimation, and out of sample forecasting, genetic algorithms (GA) as an estimator outperform …
Estimating The Non-Existent Mean And Variance Of The F-Distribution By Simulation, Hamid Reza Kamali, Parisa Shahnazari-Shahrezaei
Estimating The Non-Existent Mean And Variance Of The F-Distribution By Simulation, Hamid Reza Kamali, Parisa Shahnazari-Shahrezaei
Journal of Modern Applied Statistical Methods
In theory, all moments of some probability distributions do not necessarily exist. In the other words, they may be infinite or undefined. One of these distributions is the F-distribution whose mean and variance have not been defined for the second degree of freedom less than 3 and 5, respectively. In some cases, a large statistical population having an F-distribution may exist and the aim is to obtain its mean and variance which are an estimation of the non-existent mean and variance of F-distribution. This article considers a large sample F-distribution to estimate its non-existent mean and variance using Simul8 simulation …
Ridge Regression Based On Some Robust Estimators, Hatice Samkar, Ozlem Alpu
Ridge Regression Based On Some Robust Estimators, Hatice Samkar, Ozlem Alpu
Journal of Modern Applied Statistical Methods
Robust ridge methods based on M, S, MM and GM estimators are examined in the presence of multicollinearity and outliers. GMWalker, using the LS estimator as the initial estimator is used. S and MM estimators are also used as initial estimators with the aim of evaluating the two alternatives as biased robust methods.
A Flexible Method For Testing Independence In Two-Way Contingency Tables, Peyman Jafari, Noori Akhtar-Danesh, Zahra Bagheri
A Flexible Method For Testing Independence In Two-Way Contingency Tables, Peyman Jafari, Noori Akhtar-Danesh, Zahra Bagheri
Journal of Modern Applied Statistical Methods
A flexible approach for testing association in two-way contingency tables is presented. It is simple, does not assume a specific form for the association and is applicable to tables with nominal-by-nominal, nominal-by-ordinal, and ordinal-by-ordinal classifications.
Statistical And Mathematical Modeling Versus Nhst? There’S No Competition!, Joseph Lee Rodgers
Statistical And Mathematical Modeling Versus Nhst? There’S No Competition!, Joseph Lee Rodgers
Journal of Modern Applied Statistical Methods
Some of Robinson & Levin’s critique of Rodgers (2010) is cogent, helpful, and insightful – although limiting. Recent methodology has advanced through the development of structural equation modeling, multi-level modeling, missing data methods, hierarchical linear modeling, categorical data analysis, as well as the development of many dedicated and specific behavioral models. These methodological approaches are based on a revised epistemological system, and have emerged naturally, without the need for task forces, or even much self-conscious discussion. The original goal was neither to develop nor promote a modeling revolution. That has occurred; I documented its development and its status. Two organizing …
Effect Of Measurement Errors On The Separate And Combined Ratio And Product Estimators In Stratified Random Sampling, Housila P. Singh, Namrata Karpe
Effect Of Measurement Errors On The Separate And Combined Ratio And Product Estimators In Stratified Random Sampling, Housila P. Singh, Namrata Karpe
Journal of Modern Applied Statistical Methods
Separate and combined ratio, product and difference estimators are introduced for population mean μY of a study variable Y using auxiliary variable X in stratified sampling when the observations are contaminated with measurement errors. The bias and mean squared error of the proposed estimators have been derived under large sample approximation and their properties are analyzed. Generalized versions of these estimators are given along with their properties.
Recommended Sample Size For Conducting Exploratory Factor Analysis On Dichotomous Data, Robert H. Pearson, Daniel J. Mundform
Recommended Sample Size For Conducting Exploratory Factor Analysis On Dichotomous Data, Robert H. Pearson, Daniel J. Mundform
Journal of Modern Applied Statistical Methods
Minimum sample sizes are recommended for conducting exploratory factor analysis on dichotomous data. A Monte Carlo simulation was conducted, varying the level of communalities, number of factors, variable-to-factor ratio and dichotomization threshold. Sample sizes were identified based on congruence between rotated population and sample factor loadings.
Incidence And Prevalence For A Triply Censored Data, Hilmi F. Kittani
Incidence And Prevalence For A Triply Censored Data, Hilmi F. Kittani
Journal of Modern Applied Statistical Methods
The model introduced for the natural history of a progressive disease has four disease states which are expressed as a joint distribution of three survival random variables. Covariates are included in the model using Cox’s proportional hazards model with necessary assumptions needed. Effects of the covariates are estimated and tested. Formulas for incidence in the preclinical, clinical and death states are obtained, and prevalence formulas are obtained for the preclinical and clinical states. Estimates of the sojourn times in the preclinical and clinical states are obtained.
Robust Estimators In Logistic Regression: A Comparative Simulation Study, Sanizah Ahmad, Norazan Mohamed Ramli, Habshah Midi
Robust Estimators In Logistic Regression: A Comparative Simulation Study, Sanizah Ahmad, Norazan Mohamed Ramli, Habshah Midi
Journal of Modern Applied Statistical Methods
The maximum likelihood estimator (MLE) is commonly used to estimate the parameters of logistic regression models due to its efficiency under a parametric model. However, evidence has shown the MLE has an unduly effect on the parameter estimates in the presence of outliers. Robust methods are put forward to rectify this problem. This article examines the performance of the MLE and four existing robust estimators under different outlier patterns, which are investigated by real data sets and Monte Carlo simulation.
Use Of Two Variables Having Common Mean To Improve The Bar-Lev, Bobovitch And Boukai Randomized Response Model, Oluseun Odumade, Sarjinder Singh
Use Of Two Variables Having Common Mean To Improve The Bar-Lev, Bobovitch And Boukai Randomized Response Model, Oluseun Odumade, Sarjinder Singh
Journal of Modern Applied Statistical Methods
A new method to improve the randomized response model due to Bar-Lev, Bobovitch and Boukai (2004) is suggested. It has been observed that if two sensitive (or non sensitive) variables exist that are related to the main study sensitive variable, then those variables could be used to construct ratio type adjustments to the usual estimator of the population mean of a sensitive variable due to Bar-Lev, Bobovitch and Boukai (2004).The relative efficiency of the proposed estimators is studied with respect to the Bar-Lev, Bobovitch and Boukai (2004) models under different situations.
Maximum Downside Semi Deviation Stochastic Programming For Portfolio Optimization Problem, Anton Abdulbasah Kamil, Khlipah Ibrahim
Maximum Downside Semi Deviation Stochastic Programming For Portfolio Optimization Problem, Anton Abdulbasah Kamil, Khlipah Ibrahim
Journal of Modern Applied Statistical Methods
Portfolio optimization is an important research field in financial decision making. The chief character within optimization problems is the uncertainty of future returns. Probabilistic methods are used alongside optimization techniques. Markowitz (1952, 1959) introduced the concept of risk into the problem and used a mean-variance model to identify risk with the volatility (variance) of the random objective. The mean-risk optimization paradigm has since been expanded extensively both theoretically and computationally. A single stage and two stage stochastic programming model with recourse are presented for risk averse investors with the objective of minimizing the maximum downside semideviation. The models employ the …
On Bayesian Shrinkage Setup For Item Failure Data Under A Family Of Life Testing Distribution, Gyan Prakash
On Bayesian Shrinkage Setup For Item Failure Data Under A Family Of Life Testing Distribution, Gyan Prakash
Journal of Modern Applied Statistical Methods
Properties of the Bayes shrinkage estimator for the parameter are studied of a family of probability density function when item failure data are available. The symmetric and asymmetric loss functions are considered for two different prior distributions. In addition, the Bayes estimates of reliability function and hazard rate are obtained and their properties are studied.
Bayesian Analysis Of Location-Scale Family Of Distributions Using S-Plus And R Software, Sheikh Parvaiz Ahmad, Aquil Ahmed, Athar Ali Khan
Bayesian Analysis Of Location-Scale Family Of Distributions Using S-Plus And R Software, Sheikh Parvaiz Ahmad, Aquil Ahmed, Athar Ali Khan
Journal of Modern Applied Statistical Methods
The Normal and Laplace’s methods of approximation for posterior density based on the location-scale family of distributions in terms of the numerical and graphical simulation are examined using S-PLUS and R Software.
Empirical Characteristic Function Approach To Goodness Of Fit Tests For The Logistic Distribution Under Srs And Rss, M. T. Alodat, S. A. Al-Subh, Kamaruzaman Ibrahim, Abdul Aziz Jemain
Empirical Characteristic Function Approach To Goodness Of Fit Tests For The Logistic Distribution Under Srs And Rss, M. T. Alodat, S. A. Al-Subh, Kamaruzaman Ibrahim, Abdul Aziz Jemain
Journal of Modern Applied Statistical Methods
The integral of the squares modulus of the difference between the empirical characteristic function and the characteristic function of the hypothesized distribution is used by Wong and Sim (2000) to test for goodness of fit. A weighted version of Wong and Sim (2000) under ranked set sampling, a sampling technique introduced by McIntyre (1952), is examined. Simulations that show the ranked set sampling counterpart of Wong and Sim (2000) is more powerful.
Bayesian Analysis For Component Manufacturing Processes, L. V. Nandakishore
Bayesian Analysis For Component Manufacturing Processes, L. V. Nandakishore
Journal of Modern Applied Statistical Methods
In manufacturing processes various machines are used to produce the same product. Based on the age, make, etc., of the machines the output may not always follow the same distribution. An attempt is made to introduce Bayesian techniques for a two machine problem. Two cases are presented in this article.
Neighbor Balanced Block Designs For Two Factors, Seema Jaggi, Cini Varghese, N. R. Abeynayake
Neighbor Balanced Block Designs For Two Factors, Seema Jaggi, Cini Varghese, N. R. Abeynayake
Journal of Modern Applied Statistical Methods
The concept of Neighbor Balanced Block (NBB) designs is defined for the experimental situation where the treatments are combinations of levels of two factors and only one of the factors exhibits a neighbor effect. Methods of constructing complete NBB designs for two factors in a plot that is strongly neighbor balanced for one factor are obtained. These designs are variance balanced for estimating the direct effects of contrasts pertaining to combinations of levels of both the factors. An incomplete NBB design for two factors is also presented and is found to be partially variance balanced with three associate classes.
Ann Forecasting Models For Ise National-100 Index, Ozer Ozdemir, Atilla Aslanargun, Senay Asma
Ann Forecasting Models For Ise National-100 Index, Ozer Ozdemir, Atilla Aslanargun, Senay Asma
Journal of Modern Applied Statistical Methods
Prediction of the outputs of real world systems with accuracy and high speed is crucial in financial analysis due to its effects on worldwide economics. Because the inputs of the financial systems are timevarying functions, the development of algorithms and methods for modeling such systems cannot be neglected. The most appropriate forecasting model for the ISE national-100 index was investigated. Box- Jenkins autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are considered by using several evaluations. Results showed that the ANN model with linear architecture better fits the candidate data.