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Articles 301 - 330 of 1133
Full-Text Articles in Statistical Theory
A Spatial Analytical Framework For Examining Road Traffic Crashes, Grace O. Korter
A Spatial Analytical Framework For Examining Road Traffic Crashes, Grace O. Korter
Journal of Modern Applied Statistical Methods
A number of different modeling techniques have been used to examine road traffic crashes for analytic and predictive purposes. Map-based spatial analysis is introduced. Applications are given which show the power in a combination of existing exploratory and statistical methods.
Jmasm38: Confidence Intervals For Kendall's Tau With Small Samples (Spss), David A. Walker
Jmasm38: Confidence Intervals For Kendall's Tau With Small Samples (Spss), David A. Walker
Journal of Modern Applied Statistical Methods
A syntax program, not readily expedient in statistical software such as SPSS, is provided for an application of confidence interval estimates with Kendall’s tau-b for small samples.
An Evaluation Of Pareto, Lognormal And Pps Distributions: The Size Distribution Of Cities In Kerala, India, Christopher A. Vallabados, Subbarayan A. Arumugam
An Evaluation Of Pareto, Lognormal And Pps Distributions: The Size Distribution Of Cities In Kerala, India, Christopher A. Vallabados, Subbarayan A. Arumugam
Journal of Modern Applied Statistical Methods
The Pareto-Positive Stable (PPS) distribution is introduced as a new model for describing city size data of a region in a country. The PPS distribution provides a flexible model for fitting the entire range of a set of city size data and the classical Pareto and Zipf distributions are included as a particular case.
Determination Of Optimal Tightened Normal Tightened Plan Using A Genetic Algorithm, Sampath Sundaram, Deepa S. Parthasarathy
Determination Of Optimal Tightened Normal Tightened Plan Using A Genetic Algorithm, Sampath Sundaram, Deepa S. Parthasarathy
Journal of Modern Applied Statistical Methods
Designing a tightened normal tightened sampling plan requires sample sizes and acceptance number with switching criterion. An evolutionary algorithm, the genetic algorithm, is designed to identify optimal sample sizes and acceptance number of a tightened normal tightened sampling plan for a specified consumer’s risk, producer’s risk, and switching criterion. Optimal sample sizes and acceptance number are obtained by implementing the genetic algorithm. Tables are reported for various choices of switching criterion, consumer’s quality level, and producer’s quality level.
Model-Based Outlier Detection System With Statistical Preprocessing, D. Asir Antony Gnana Singh, E. Jebalamar Leavline
Model-Based Outlier Detection System With Statistical Preprocessing, D. Asir Antony Gnana Singh, E. Jebalamar Leavline
Journal of Modern Applied Statistical Methods
Reliability, lack of error, and security are important improvements to quality of service. Outlier detection is a process of detecting the erroneous parts or abnormal objects in defined populations, and can contribute to secured and error-free services. Outlier detection approaches can be categorized into four types: statistic-based, unsupervised, supervised, and semi-supervised. A model-based outlier detection system with statistical preprocessing is proposed, taking advantage of the statistical approach to preprocess training data and using unsupervised learning to construct the model. The robustness of the proposed system is evaluated using the performance evaluation metrics sum of squared error (SSE) and time to …
Analyzing Different Sampling Designs (Sas), Ying Lu
Analyzing Different Sampling Designs (Sas), Ying Lu
Journal of Modern Applied Statistical Methods
Various sampling designs are reviewed within the framework of probability sampling. SAS® code to estimate means and proportions, and their standard errors, using different sampling designs are illustrated using example data sets.
Bayesian Estimation Of P[Y < X] Based On Record Values From The Lomax Distribution And Mcmc Technique, Mohamed A. W Mahmoud, Rashad M. El-Sagheer, Ahmed A. Soliman, Ahmed H. Abd Ellah
Bayesian Estimation Of P[Y < X] Based On Record Values From The Lomax Distribution And Mcmc Technique, Mohamed A. W Mahmoud, Rashad M. El-Sagheer, Ahmed A. Soliman, Ahmed H. Abd Ellah
Journal of Modern Applied Statistical Methods
Our interest is in estimating the stress-strength reliability R = P[Y < X], where X and Y follow the Lomax distribution with common scale parameter. We discuss the problem in the situation where the stress measurements and the strength measurements are both in terms of records. Firstly, we obtain the MLE of R in general case (the common scale parameter is unknown). The MLE of the three unknown parameters can be obtained by solving one non-linear equation. We provide a simple fixed point type algorithm to find the MLE. We propose percentile bootstrap confidence intervals of R. A Bayes …
Graphing Effects As Fuzzy Numbers In Meta-Analysis, Christopher G. Thompson
Graphing Effects As Fuzzy Numbers In Meta-Analysis, Christopher G. Thompson
Journal of Modern Applied Statistical Methods
Prior to quantitative analyses, meta-analysts often explore descriptive characteristics of effect sizes. A graphic is proposed that treats effect sizes as fuzzy numbers. This plot can provide meta-analysts with such information such as heterogeneity of effects, precision of estimates, possible clusters, and existence of outliers.
Principal Component Preliminary Test Estimator In The Linear Regression Model, Sivarajah Arumairajan, Pushpakanthie Wijekoon
Principal Component Preliminary Test Estimator In The Linear Regression Model, Sivarajah Arumairajan, Pushpakanthie Wijekoon
Journal of Modern Applied Statistical Methods
A Preliminary Test Estimator is introduced based on Principal Component Regression Estimator defined in the linear regression model when the stochastic restrictions are available in addition to the sample information, and when the explanatory variables are multicollinear. It is further developed as a large sample preliminary test estimator by using Wald (WA), Likelihood Ratio (LR), and Lagrangian Multiplier (LM) tests. Stochastic properties of this estimator based on F test as well as WA, LR, and LM tests are derived, and the performance of the estimator is compared using WA, LR, and LM tests with respect to Mean Square Error Matrix …
New Procedures Of Estimating Proportion And Sensitivity Using Randomized Response In A Dichotomous Finite Population, Tanveer A. Tarray, Housila P. Singh
New Procedures Of Estimating Proportion And Sensitivity Using Randomized Response In A Dichotomous Finite Population, Tanveer A. Tarray, Housila P. Singh
Journal of Modern Applied Statistical Methods
The problem of estimating the population proportion possessing a sensitive attribute using simple random sampling with replacement (SRSWR) is advocated. Two new procedures are proposed. The suggested models are more efficient than the Huang (2004) randomized response technique under some realistic conditions. Numerical and graphic illustrations are given.
Generalized Linear Model Analyses For Treatment Group Equality When Data Are Non-Normal, Harvey J. Kesleman, Abdul R. Othman, Rand R. Wilcox
Generalized Linear Model Analyses For Treatment Group Equality When Data Are Non-Normal, Harvey J. Kesleman, Abdul R. Othman, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
One of the validity conditions of classical test statistics (e.g., Student’s t-test, the ANOVA and MANOVA F-tests) is that data be normally distributed in the populations. When this and/or other derivational assumptions do not hold the classical test statistic can be prone to too many Type I errors (i.e., falsely rejecting too often) and/or have low power (i.e., failing to reject when the null hypothesis is false) to detect treatment effects when they are present. However, alternative procedures are available for assessing equality of treatment group effects when data are non-normal. For example, researchers can use robust estimators …
Liu-Type Logistic Estimators With Optimal Shrinkage Parameter, Yasin Asar
Liu-Type Logistic Estimators With Optimal Shrinkage Parameter, Yasin Asar
Journal of Modern Applied Statistical Methods
Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when the optimal shrinkage parameter is used in the Liu-type estimators, along with an application of real case data.
The Xgamma Distribution: Statistical Properties And Application, Subhradev Sen, Sudhansu S. Maiti, N. Chandra
The Xgamma Distribution: Statistical Properties And Application, Subhradev Sen, Sudhansu S. Maiti, N. Chandra
Journal of Modern Applied Statistical Methods
A new probability distribution, the xgamma distribution, is proposed and studied. The distribution is generated as a special finite mixture of exponential and gamma distributions and hence the name proposed. Various mathematical, structural, and survival properties of the xgamma distribution are derived, and it is found that in many cases the xgamma has more flexibility than the exponential distribution. To evaluate the comparative behavior, stochastic ordering of the distribution is studied. To estimate the model parameter, the method of moment and the method of maximum likelihood estimation are proposed. A simulation algorithm to generate random samples from the xgamma distribution …
Analysis And Modeling Of Statistical Properties Of Fmdfb Subband Coefficients, E. Jebamalar Leavline, Sutha Shunmugam
Analysis And Modeling Of Statistical Properties Of Fmdfb Subband Coefficients, E. Jebamalar Leavline, Sutha Shunmugam
Journal of Modern Applied Statistical Methods
Fast Multiscale Directional Filter Bank (FMDFB) is an image representation scheme used in several image processing applications. The statistical nature of the FMDFB subbands is analyzed, and a mathematical model of FMDFB coefficients is proposed. Experimental results are justified by goodness-of-fit tests.
Jmasm37: Simple Response Surface Methodology Using Rsreg (Sas), Wan Muhamad Amir, Mohamad Shafiq, Kasypi Mokhtar, Nor Azlida Aleng, Hanafi A.Rahim, Zalila Ali
Jmasm37: Simple Response Surface Methodology Using Rsreg (Sas), Wan Muhamad Amir, Mohamad Shafiq, Kasypi Mokhtar, Nor Azlida Aleng, Hanafi A.Rahim, Zalila Ali
Journal of Modern Applied Statistical Methods
Response surface methodology (RSM) can be used when the response variable, y, is influenced by several variables, x’s. When treatments take the form of quantitative values, then the true relationship between response variables and independent variables might be known. Examples are given in SAS.
Generalized Singular Value Decomposition With Additive Components, Stan Lipovetsky
Generalized Singular Value Decomposition With Additive Components, Stan Lipovetsky
Journal of Modern Applied Statistical Methods
The singular value decomposition (SVD) technique is extended to incorporate the additive components for approximation of a rectangular matrix by the outer products of vectors. While dual vectors of the regular SVD can be expressed one via linear transformation of the other, the modified SVD corresponds to the general linear transformation with the additive part. The method obtained can be related to the family of principal component and correspondence analyses, and can be reduced to an eigenproblem of a specific transformation of a data matrix. This technique is applied to constructing dual eigenvectors for data visualizing in a two dimensional …
Almost Unbiased Estimator Using Known Value Of Population Parameter(S) In Sample Surveys, Rajesh Singh, S.B. Gupta, Sachin Malik
Almost Unbiased Estimator Using Known Value Of Population Parameter(S) In Sample Surveys, Rajesh Singh, S.B. Gupta, Sachin Malik
Journal of Modern Applied Statistical Methods
An almost unbiased estimator using known value of some population parameter(s) is proposed. A class of estimators is defined which includes Singh and Solanki (2012) and Sahai and Ray (1980), Sisodiya and Dwivedi (1981), Singh, Cauhan, Sawan, and Smarandache (2007), Upadhyaya and Singh (1984), Singh and Tailor (2003) estimators. Under simple random sampling without replacement (SRSWOR) scheme the expressions for bias and mean square error (MSE) are derived. Numerical illustrations are given.
A Comparison Of Estimation Methods For Nonlinear Mixed-Effects Models Under Model Misspecification And Data Sparseness: A Simulation Study, Jeffrey R. Harring, Junhui Liu
A Comparison Of Estimation Methods For Nonlinear Mixed-Effects Models Under Model Misspecification And Data Sparseness: A Simulation Study, Jeffrey R. Harring, Junhui Liu
Journal of Modern Applied Statistical Methods
A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification.
Variable Selection In Regression Using Multilayer Feedforward Network, Tejaswi S. Kamble, Dattatraya N. Kashid
Variable Selection In Regression Using Multilayer Feedforward Network, Tejaswi S. Kamble, Dattatraya N. Kashid
Journal of Modern Applied Statistical Methods
The selection of relevant variables in the model is one of the important problems in regression analysis. Recently, a few methods were developed based on a model free approach. A multilayer feedforward neural network model was proposed for developing variable selection in regression. A simulation study and real data were used for evaluating the performance of proposed method in the presence of outliers, and multicollinearity.
Jmasm39: Algorithm For Combining Robust And Bootstrap In Multiple Linear Model Regression (Sas), Wan Muhamad Amir, Mohamad Shafiq, Hanafi A.Rahim, Puspa Liza, Azlida Aleng, Zailani Abdullah
Jmasm39: Algorithm For Combining Robust And Bootstrap In Multiple Linear Model Regression (Sas), Wan Muhamad Amir, Mohamad Shafiq, Hanafi A.Rahim, Puspa Liza, Azlida Aleng, Zailani Abdullah
Journal of Modern Applied Statistical Methods
The aim of bootstrapping is to approximate the sampling distribution of some estimator. An algorithm for combining method is given in SAS, along with applications and visualizations.
Jmasm35: A Percentile-Based Power Method: Simulating Multivariate Non-Normal Continuous Distributions (Sas), Jennifer Koran, Todd C. Headrick
Jmasm35: A Percentile-Based Power Method: Simulating Multivariate Non-Normal Continuous Distributions (Sas), Jennifer Koran, Todd C. Headrick
Journal of Modern Applied Statistical Methods
The conventional power method transformation is a moment-matching technique that simulates non-normal distributions with controlled measures of skew and kurtosis. The percentile-based power method is an alternative that uses the percentiles of a distribution in lieu of moments. This article presents a SAS/IML macro that implements the percentile-based power method.
Structural Properties Of Transmuted Weibull Distribution, Kaisar Ahmad, S. P. Ahmad, A. Ahmed
Structural Properties Of Transmuted Weibull Distribution, Kaisar Ahmad, S. P. Ahmad, A. Ahmed
Journal of Modern Applied Statistical Methods
The transmuted Weibull distribution, and a related special case, is introduced. Estimates of parameters are obtained by using a new method of moments.
New Entropy Estimators With Smaller Root Mean Squared Error, Amer Ibrahim Al-Omari
New Entropy Estimators With Smaller Root Mean Squared Error, Amer Ibrahim Al-Omari
Journal of Modern Applied Statistical Methods
New estimators of entropy of continuous random variable are suggested. The proposed estimators are investigated under simple random sampling (SRS), ranked set sampling (RSS), and double ranked set sampling (DRSS) methods. The estimators are compared with Vasicek (1976) and Al-Omari (2014) entropy estimators theoretically and by simulation in terms of the root mean squared error (RMSE) and bias values. The results indicate that the suggested estimators have less RMSE and bias values than their competing estimators introduced by Vasicek (1976) and Al-Omari (2014).
An Empirical Study On Different Ranking Methods For Effective Data Classification, Ilangovan Sangaiah, A. Vincent Antony Kumar, Appavu Balamurugan
An Empirical Study On Different Ranking Methods For Effective Data Classification, Ilangovan Sangaiah, A. Vincent Antony Kumar, Appavu Balamurugan
Journal of Modern Applied Statistical Methods
Ranking is the attribute selection technique used in the pre-processing phase to emphasize the most relevant attributes which allow models of classification simpler and easy to understand. It is a very important and a central task for information retrieval, such as web search engines, recommendation systems, and advertisement systems. A comparison between eight ranking methods was conducted. Ten different learning algorithms (NaiveBayes, J48, SMO, JRIP, Decision table, RandomForest, Multilayerperceptron, Kstar) were used to test the accuracy. The ranking methods with different supervised learning algorithms give different results for balanced accuracy. It was shown the selection of ranking methods could be …
Caution For Software Use Of New Statistical Methods (R), Akiva J. Lorenz, Barry S. Markman, Shlomo Sawilowsky
Caution For Software Use Of New Statistical Methods (R), Akiva J. Lorenz, Barry S. Markman, Shlomo Sawilowsky
Journal of Modern Applied Statistical Methods
Open source programming languages such as R allow statisticians to develop and rapidly disseminate advanced procedures, but sometimes at the expense of a proper vetting process. A new example is the least trimmed squares regression available in R’s lqs() in the MASS library. It produces pretty regression lines, particularly in the presence of outliers. However, this procedure lacks a defined standard error, and thus it should be avoided.
Inferences About The Skipped Correlation Coefficient: Dealing With Heteroscedasticity And Non-Normality, Rand Wilcox
Inferences About The Skipped Correlation Coefficient: Dealing With Heteroscedasticity And Non-Normality, Rand Wilcox
Journal of Modern Applied Statistical Methods
A common goal is testing the hypothesis that Pearson’s correlation is zero and typically this is done based on Student’s T test. There are, however, several well-known concerns. First, Student’s T is sensitive to heteroscedasticity. That is, when it rejects, it is reasonable to conclude that there is dependence, but in terms of making a decision about the strength of the association, it is unsatisfactory. Second, Pearson’s correlation is not robust: it can poorly reflect the strength of the association. Even a single outlier can have a tremendous impact on the usual estimate of Pearson’s correlation, which can result in …
Front Matter, Jmasm Editors
Jmasm34: Two Group Program For Cohen's D, Hedges’ G, Η2, Radj2, Ω2, Ɛ2, Confidence Intervals, And Power, David A. Walker
Jmasm34: Two Group Program For Cohen's D, Hedges’ G, Η2, Radj2, Ω2, Ɛ2, Confidence Intervals, And Power, David A. Walker
Journal of Modern Applied Statistical Methods
The purpose of this research is to provide an application for users interested in a SPSS syntax program to determine an array of commonly-employed effect sizes and confidence intervals not readily available in SPSS functionality, such as the standardized mean difference and r-related squared indices, for a between-group design.
Monte Carlo Comparison Of The Parameter Estimation Methods For The Two-Parameter Gumbel Distribution, Demet Aydin, Birdal Şenoğlu
Monte Carlo Comparison Of The Parameter Estimation Methods For The Two-Parameter Gumbel Distribution, Demet Aydin, Birdal Şenoğlu
Journal of Modern Applied Statistical Methods
The performances of the seven different parameter estimation methods for the Gumbel distribution are compared with numerical simulations. Estimation methods used in this study are the method of moments (ME), the method of maximum likelihood (ML), the method of modified maximum likelihood (MML), the method of least squares (LS), the method of weighted least squares (WLS), the method of percentile (PE) and the method of probability weighted moments (PWM). Performance of the estimators is compared with respect to their biases, MSE and deficiency (Def) values via Monte-Carlo simulation. A Monte Carlo Simulation study showed that the method of PWM was …
Contrails: Causal Inference Using Propensity Scores, Dean S. Barron
Contrails: Causal Inference Using Propensity Scores, Dean S. Barron
Journal of Modern Applied Statistical Methods
Contrails are clouds caused by airplane exhausts, which geologists contend decrease daily temperature ranges on Earth. Following the 2001 World Trade Center attack, cancelled domestic flights triggered the first absence of contrails in decades. Resultant exceptional data capacitated causal inference analysis by propensity score matching. Estimated contrail effect was 6.8981°F.