Statistical Theory Commons^™

Monte Carlo Simulation Design For Evaluating Normal-Based Control Chart Properties, John N. Dyer

Journal of Modern Applied Statistical Methods

The advent of more complicated control charting schemes has necessitated the use of Monte Carlo simulation (MCS) methods. Unfortunately, few sources exist to study effective design and validation of MCS methods related to control charting. This paper describes the design, issues, considerations and limitations for conducting normal-based control chart MCS studies, including choice of random number generator, simulation size requirements, and accuracy/error in simulation estimation. This paper also describes two design strategies for MCS for control chart evaluations and provides the programming code. As a result, this paper hopes to establish de facto MCS schemes aimed at guiding researchers and …

Latent Variable Model For Weight Gain Prevention Data With Informative Intermittent Missingness, Li Qin, Lisa Weissfeld, Michele Levine, Marsha Marcus, Feng Dai

Journal of Modern Applied Statistical Methods

Missing data is a common problem in longitudinal studies because of the characteristics of repeated measurements. Herein is proposed a latent variable model for nonignorable intermittent missing data in which the latent variables are used as random effects in modeling and link longitudinal responses and missingness process. In this methodology, the latent variables are assumed to be normally distributed with zero-mean, and the values of variance-covariance are calculated through maximum likelihood estimations. Parameter estimates and standard errors of the proposed method are compared with the mixed model and the complete-case analysis in the simulations and the application to the weight …

E-Bayesian Estimation Of The Parameter Of The Logarithmic Series Distribution, Parviz Nasiri, Hassan Esfandyarifar

Journal of Modern Applied Statistical Methods

E-Bayesian estimation is introduced to estimate the parameter of logarithmic series distribution. In addition, E-Bayesian, Bayesian and maximum likelihood estimation with through applying mean squared error.

Jmasm41: An Alternative Method For Multiple Linear Model Regression Modeling, A Technical Combining Of Robust, Bootstrap And Fuzzy Approach (Sas), Wan Muhamad Amir W Ahmad, Mohamad Arif Awang Nawi, Nor Azlida Aleng, Mohamad Shafiq

Journal of Modern Applied Statistical Methods

Research on modeling is becoming popular nowadays, there are several of analyses used in research for modeling and one of them is known as applied multiple linear regressions (MLR). To obtain a bootstrap, robust and fuzzy multiple linear regressions, an experienced researchers should be aware the correct method of statistical analysis in order to get a better improved result. The main idea of bootstrapping is to approximate the entire sampling distribution of some estimator. To achieve this is by resampling from our original sample. In this paper, we emphasized on combining and modeling using bootstrapping, robust and fuzzy regression methodology. …

Misspecification Of Variants Of Autoregressive Garch Models And Effect On In-Sample Forecasting, Olusanya E. Olubusoye, Olaoluwa S. Yaya, Oluwadare O. Ojo

Journal of Modern Applied Statistical Methods

Generally, in empirical financial studies, the determination of the true conditional variance in GARCH modelling is largely subjective. In this paper, we investigate the consequences of choosing a wrong conditional variance specification. The methodology involves specifying a true conditional variance and then simulating data to conform to the true specification. The estimation is then carried out using the true specification and other plausible specification that are appealing to the researcher, using model and forecast evaluation criteria for assessing performance. The results show that GARCH model could serve as better alternative to other asymmetric volatility models.

Improved Ridge Estimator In Linear Regression With Multicollinearity, Heteroscedastic Errors And Outliers, Ashok Vithoba Dorugade

Journal of Modern Applied Statistical Methods

This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated by Monte Carlo simulation. We examine the performance of the proposed estimators compared with other well-known estimators for the model with heteroscedastics and/or correlated errors, outlier observations, non-normal errors and suffer from the problem of multicollinearity. It is shown that proposed estimators have a smaller MSE than the ordinary least squared estimator (LS), Hoerl and Kennard (1970) estimator (RR), jackknifed modified ridge (JMR) estimator, and Jackknifed Ridge M‑estimator (JRM).

Multicollinearity And A Ridge Parameter Estimation Approach, Ghadban Khalaf, Mohamed Iguernane

Journal of Modern Applied Statistical Methods

One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importance of independent variables in determining their predictive ability. However, in practical applications, inference about the coefficients of regression can be difficult because the independent variables are correlated and multicollinearity causes instability in the coefficients. A new estimator of ridge regression parameter is proposed and evaluated by simulation techniques in terms of mean squares error (MSE). Results of the simulation study indicate that the suggested estimator dominates ordinary least squares (OLS) estimator and other ridge estimators with respect to …

Estimation Of Parameters Of Misclassified Size Biased Borel Distribution, Bhaktida S. Trivedi, M. N. Patel

Journal of Modern Applied Statistical Methods

A misclassified size-biased Borel Distribution (MSBBD), where some of the observations corresponding to x = c + 1 are wrongly reported as x = c with probability α, is defined. Various estimation methods like the method of maximum likelihood (ML), method of moments, and the Bayes estimation for the parameters of the MSBB distribution are used. The performance of the estimators are studied using simulated bias and simulated risk. Simulation studies are carried out for different values of the parameters and sample size.

Developing Bayesian-Based Confidence Bounds For Non-Identically Distributed Observations Using The Lyapunov Condition, Garry M. Jacyna, Scott L. Rosen

Journal of Modern Applied Statistical Methods

The purpose of this paper is to establish a direct method for assessing the confidence in the detection and identification probabilities for segmented observations that are not identically distributed across assigned segments within a region. This paper arrives at easily computable confidence intervals by showing through mathematical analysis that:

I. The probability of successful detection within each test segment can be characterized by a Beta distribution;
II. The distribution of a weighted sum of independent but non-identically distributed sample means is asymptotically Normally distributed by the Lyapunov variant of the Central Limit Theorem, i.e., the approximation improves as the number …

Jmasm40: Monte Carlo Simulations For Structural Equation Modelling (Revolution R), Sarah A. Rose, Barry Markman

Journal of Modern Applied Statistical Methods

Revolution R code is presented to setup Structural Equation Model (SEM) for a Monte Carlo study. The example is a comparison of different fit indices.

Reflections Concerning Recent Ban On Nhst And Confidence Intervals, Grayson L. Baird, Sunny R. Duerr

Journal of Modern Applied Statistical Methods

This letter addresses some of the immediate consequences of Basic and Applied Social Psychology’s (BASP) ban on null hypothesis significance testing (NHST) and confidence intervals. The letter concludes with three suggestions to improve research in general.

A New Test For Correlation On Bivariate Nonnormal Distributions, Ping Wang, Ping Sa

Journal of Modern Applied Statistical Methods

A new method to conduct a right-tailed test for the correlation on bivariate non-normal distribution is proposed. The comparative simulation study shows that the new test controls the type I error rates well for all the distributions considered. An investigation of the power performance is also provided.

Jmasm42: An Alternative Algorithm And Programming Implementation For Least Absolute Deviation Estimator Of The Linear Regression Models (R), Suraju Olaniyi Ogundele, J. I. Mbegbu, C. R. Nwosu

Journal of Modern Applied Statistical Methods

We propose a least absolute deviation estimation method that produced a least absolute deviation estimator of parameter of the linear regression model. The method is as accurate as existing method.

Application Of Esscher Transformed Laplace Distribution In Microarray Gene Expression Data, Shanmugasundaram Devika, Sebastian George, Lakshmanan Jeyaseelan

Journal of Modern Applied Statistical Methods

Microarrays allow the study of the expression profile of hundreds to thousands of genes simultaneously. These expressions could be from treated samples and the healthy controls. The Esscher transformed Laplace distribution is used to fit microarray expression data as compared to Normal and Laplace distributions. The Maximum Likelihood Estimation procedure is used to estimate the parameters of the distribution. R codes are developed to implement the estimation procedure. A simulation study is carried out to test the performance of the algorithm. AIC and BIC criterion are used to compare the distributions. It is shown that the fit of the Esscher …

The Goldilocks Dilemma: Impacts Of Multicollinearity -- A Comparison Of Simple Linear Regression, Multiple Regression, And Ordered Variable Regression Models, Grayson L. Baird, Stephen L. Bieber

Journal of Modern Applied Statistical Methods

A common consideration concerning the application of multiple linear regression is the lack of independence among predictors (multicollinearity). The main purpose of this article is to introduce an alternative method of regression originally outlined by Woolf (1951), which completely eliminates the relatedness between the predictors in a multiple predictor setting.

Solution To The Multicollinearity Problem By Adding Some Constant To The Diagonal, Hanan Duzan, Nurul Sima Binti Mohamaed Shariff

Journal of Modern Applied Statistical Methods

Ridge regression is an alternative to ordinary least-squares (OLS) regression. It is believed to be superior to least-squares regression in the presence of multicollinearity. The robustness of this method is investigated and comparison is made with the least squares method through simulation studies. Our results show that the system stabilizes in a region of k, where k is a positive quantity less than one and whose values depend on the degree of correlation between the independent variables. The results also illustrate that k is a linear function of the correlation between the independent variables.

Symmetric Variants Of Logistic Smooth Transition Autoregressive Models: Monte Carlo Evidences, Olaoluwa S. Yaya, Olanrewaju I. Shittu

Journal of Modern Applied Statistical Methods

The Smooth Transition Autoregressive (STAR) models are becoming popular in modeling economic and financial time series. The asymmetric type of the model is the Logistic STAR (LSTAR) model, which is limited in its applications as a result of its asymmetric property, which makes it suitable for modelling specific macroeconomic time series. This study was designed to develop the Absolute Error LSTAR (AELSTAR) and Quadratic LSTAR (QLSTAR) models for improving symmetry and performance in terms of model fitness. Modified Teräsvirta’s Procedure (TP) and Escribano and Jordá's Procedure (EJP) were used to test for nonlinearity in the series. The performance of the …

Factorial Invariance Testing Under Different Levels Of Partial Loading Invariance Within A Multiple Group Confirmatory Factor Analysis Model, Brian F. French, Holmes Finch

Journal of Modern Applied Statistical Methods

Scalar invariance in factor models is important for comparing latent means. Little work has focused on invariance testing for other model parameters under various conditions. This simulation study assesses how partial factorial invariance influences invariance testing for model parameters. Type I error inflation and parameter bias were observed.

Jmasm36: Nine Pseudo R^2 Indices For Binary Logistic Regression Models (Spss), David A. Walker, Thomas J. Smith

Journal of Modern Applied Statistical Methods

This syntax program is an applied complement to Veall and Zimmermann (1994), Menard (2000), and Smith and McKenna (2013) and produces nine pseudo R² indices, not readily accessible in statistical software such as SPSS, which are used to describe the results from binary logistic regression analyses.

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

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

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

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

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

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

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

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

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

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

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 …