Automated Sea State Classification From Parameterization Of Survey Observations And Wave-Generated Displacement Data,
2016
University of New Orleans, New Orleans
Automated Sea State Classification From Parameterization Of Survey Observations And Wave-Generated Displacement Data, Jason A. Teichman
LSU New Orleans Theses and Dissertations
Sea state is a subjective quantity whose accuracy depends on an observer’s ability to translate local wind waves into numerical scales. It provides an analytical tool for estimating the impact of the sea on data quality and operational safety. Tasks dependent on the characteristics of local sea surface conditions often require accurate and immediate assessment. An attempt to automate sea state classification using eleven years of ship motion and sea state observation data is made using parametric modeling of distribution-based confidence and tolerance intervals and a probabilistic model using sea state frequencies. Models utilizing distribution intervals are not able to …
Population Projection And Habitat Preference Modeling Of The Endangered James Spinymussel (Pleurobema Collina),
2016
James Madison University
Population Projection And Habitat Preference Modeling Of The Endangered James Spinymussel (Pleurobema Collina), Marisa Draper
Senior Honors Projects, 2010-2019
The James Spinymussel (Pleurobema collina) is an endangered mussel species at the top of Virginia’s conservation list. The James Spinymussel plays a critical role in the environment by filtering and cleaning stream water while providing shelter and food for macroinvertebrates; however, conservation efforts are complicated by the mussels’ burrowing behavior, camouflage, and complex life cycle. The goals of the research conducted were to estimate detection probabilities that could be used to predict species presence and facilitate field work, and to track individually marked mussels to test for habitat preferences. Using existing literature and mark-recapture field data, these goals were accomplished …
Application Of Esscher Transformed Laplace Distribution In Microarray Gene Expression Data,
2016
Christian Medical College
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,
2016
University of Wyoming
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,
2016
Universiti Sains Islam Malaysia
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,
2016
University of Ibadan, Nigeria
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,
2016
Washington State University
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),
2016
Northern Illinois University
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,
2016
UNIVERSITY OF IBADAN
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),
2016
Northern Illinois University
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,
2016
S.R.M. Medical College and Research Centre, Kattankulathur, India
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,
2016
University of Madras
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,
2016
Anna University, Tiruchirappalli, India
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),
2016
Educational Testing Service
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,
2016
Al-Azhar University, Cairo, Egypt
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,
2016
Florida State University
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,
2016
Department of Mathematics and Statistics, University of Jaffna, Sri Lanka
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,
2016
School of Studies in Statistics, Vikram University Ujjain - M.P. - India.
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.
Construction Of Pair-Wise Balanced Design,
2016
Manonmaniam Sundaranar University
Construction Of Pair-Wise Balanced Design, Rajarathinam Arunachalam, Mahalakshmi Sivasubramanian, Dilip Kumar Ghosh
Journal of Modern Applied Statistical Methods
A new procedure for construction of pair wise balanced design with equal replication and un-equal block sizes based on factorial design have been evolved. Numerical illustration also provided. It was found that the constructed pair wise balanced design was found to be universal optimal.
Generalized Linear Model Analyses For Treatment Group Equality When Data Are Non-Normal,
2016
University of Manitoba
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 …
