Bayesian Approximation Techniques For Scale Parameter Of Laplace Distribution, 2019 University of Kashmir, Srinagar

#### Bayesian Approximation Techniques For Scale Parameter Of Laplace Distribution, Uzma Jan, S. P. Ahmad

*Journal of Modern Applied Statistical Methods*

The Bayesian estimation of the scale parameter of a Laplace Distribution is obtained using two approximation techniques, like Normal approximation and Tierney and Kadane (T-K) approximation, under different informative priors.

Can One Test Fit All? Responses To The Article “Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations” (Ruxton & Neuhäuser, 2018), 2019 University of South Florida

#### Can One Test Fit All? Responses To The Article “Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations” (Ruxton & Neuhäuser, 2018), Diep Nguyen, Eun Sook Kim, Yi-Hsin Chen

*Journal of Modern Applied Statistical Methods*

Responses to suggestions made by Ruxton & Neuhäuser (2018) regarding Nguyen et al. (2016) are given.

On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, 2019 Clemson University

#### On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, Patrick J. Rosopa, Alice M. Brawley, Theresa P. Atkinson, Stephen A. Robertson

*Journal of Modern Applied Statistical Methods*

Preliminary tests for homoscedasticity may be unnecessary in general linear models. Based on Monte Carlo simulations, results suggest that when testing for differences between independent slopes, the unconditional use of weighted least squares regression and HC4 regression performed the best across a wide range of conditions.

Φ-Divergence Loss-Based Artificial Neural Network, 2019 Shivaji University, Kolhapur, India

#### Φ-Divergence Loss-Based Artificial Neural Network, R. L. Salamwade, D. M. Sakate, S. K. Mathur

*Journal of Modern Applied Statistical Methods*

Artificial Neural Networks (ANNs) can fit non-linear functions and recognize patterns better than several standard techniques. Performance of ANNs is measured by using loss functions. Phi-divergence estimator is generalization of maximum likelihood estimator and it possesses all its properties. A neural network is proposed which is trained using phi-divergence loss.

A Robust Nonparametric Measure Of Effect Size Based On An Analog Of Cohen's D, Plus Inferences About The Median Of The Typical Difference, 2019 University of Southern California

#### A Robust Nonparametric Measure Of Effect Size Based On An Analog Of Cohen's D, Plus Inferences About The Median Of The Typical Difference, Rand Wilcox

*Journal of Modern Applied Statistical Methods*

The paper describes a nonparametric analog of Cohen's *d*, *Q*. It is established that a confidence interval for *Q* can be computed via a method for computing a confidence interval for the median of *D* = *X*_{1} − *X*_{2}, which in turn is related to making inferences about P(*X*_{1} < *X*_{2}).

Robust Ancova, Curvature, And The Curse Of Dimensionality, 2019 University of Southern California

#### Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox

*Journal of Modern Applied Statistical Methods*

There is a substantial collection of robust analysis of covariance (ANCOVA) methods that effectively deals with non-normality, unequal population slope parameters, outliers, and heteroscedasticity. Some are based on the usual linear model and others are based on smoothers (nonparametric regression estimators). However, extant results are limited to one or two covariates. A minor goal here is to extend a recently-proposed method, based on the usual linear model, to situations where there are up to six covariates. The usual linear model might provide a poor approximation of the true regression surface. The main goal is to suggest a method, based on ...

Logistic Regression: An Inferential Method For Identifying The Best Predictors, 2019 University of Southern California

#### Logistic Regression: An Inferential Method For Identifying The Best Predictors, Rand Wilcox

*Journal of Modern Applied Statistical Methods*

When dealing with a logistic regression model, there is a simple method for estimating the strength of the association between the *j*^{th} covariate and the dependent variable when all covariates are entered into the model. There is the issue of determining whether the *j*^{th} independent variable has a stronger or weaker association than the *k*^{th} independent variable. This note describes a method for dealing with this issue that was found to perform reasonably well in simulations.

Should We Give Up On Causality?, 2019 The Ohio State University

#### Should We Give Up On Causality?, Tom Knapp

*Journal of Modern Applied Statistical Methods*

No abstract provided.

Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, 2019 University of St Andrews

#### Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, Graeme Ruxton, Markus Neuhäuser

*Journal of Modern Applied Statistical Methods*

Nguyen et al. (2016) offered advice to researchers in the commonly-encountered situation where they are interested in testing for a difference in central tendency between two populations. Their data and the available literature support very simple advice that strikes the best balance between ease of implementation, power and reliability. Specifically, apply Satterthwaite’s test, with preliminary ranking of the data if a strong deviation from normality is expected, or is suggested by visual inspection of the data. This simple guideline will serve well except when dealing with small samples of discrete data, when more sophisticated treatment may be required.

A Strategy For Using Bias And Rmse As Outcomes In Monte Carlo Studies In Statistics, 2019 University of Minnesota - Twin Cities

#### A Strategy For Using Bias And Rmse As Outcomes In Monte Carlo Studies In Statistics, Michael Harwell

*Journal of Modern Applied Statistical Methods*

To help ensure important patterns of bias and accuracy are detected in Monte Carlo studies in statistics this paper proposes conditioning bias and root mean square error (RMSE) measures on estimated Type I and Type II error rates. A small Monte Carlo study is used to illustrate this argument.

Neural Shrubs: Using Neural Networks To Improve Decision Trees, 2019 SDSMT

#### Neural Shrubs: Using Neural Networks To Improve Decision Trees, Kyle Caudle, Randy Hoover, Aaron Alphonsus

*SDSU Data Science Symposium*

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as a poster ...

A Proficient Two-Stage Stratified Randomized Response Strategy, 2018 Islamic University of Science and Technology, Awantipora, India

#### A Proficient Two-Stage Stratified Randomized Response Strategy, Tanveer A. Tarray, Housila P. Singh

*Journal of Modern Applied Statistical Methods*

A stratified randomized response model based on R. Singh, Singh, Mangat, and Tracy (1995) improved two-stage randomized response strategy is proposed. It has an optimal allocation and large gain in precision. Conditions are obtained under which the proposed model is more efficient than R. Singh et al. (1995) and H. P. Singh and Tarray (2015) models. Numerical illustrations are also given in support of the present study.

Simple Unbalanced Ranked Set Sampling For Mean Estimation Of Response Variable Of Developmental Programs, 2018 Indian Council of Forestry Research and Education

#### Simple Unbalanced Ranked Set Sampling For Mean Estimation Of Response Variable Of Developmental Programs, Girish Chandra, Dinesh S. Bhoj, Rajiv Pandey

*Journal of Modern Applied Statistical Methods*

An unbalanced ranked set sampling (RSS) procedure on the skewed survey variable is proposed to estimate the population mean of a response variable from the area of developmental programs which are generally implemented under different phases. It is based on the unbalanced RSS under linear impacts of the program and is compared with the estimators based on simple random sampling (SRS) and balanced RSS. It is shown that the relative precision of the proposed estimator is higher than those of the estimators based on SRS and balanced RSS for three chosen skewed distributions of survey variables.

Extended Method For Several Dichotomous Covariates To Estimate The Instantaneous Risk Function Of The Aalen Additive Model, 2018 Federal University of São João del Rei

#### Extended Method For Several Dichotomous Covariates To Estimate The Instantaneous Risk Function Of The Aalen Additive Model, Luciane Teixeira Passos Giarola, Mario Javier Ferrua Vivanco, Marcelo Angelo Cirillo, Fortunato Silva Menezes

*Journal of Modern Applied Statistical Methods*

The instantaneous risk function of Aalen’s model is estimated considering dichotomous covariates, using parametric accumulated risk functions to smooth cumulative risk of Aalen by grouping the individuals into sets named parcels. This methodology can be used for data with dichotomous covariates.

Seasonal Warranty Prediction Based On Recurrent Event Data, 2018 Iowa State University

#### Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker Jr.

*Statistics Preprints*

Warranty return data from repairable systems, such as vehicles, usually result in recurrent event data. The non-homogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the than the overall population. The stratification facilitates ...

An Analysis Of Classroom Collusion Using Latent Dirichlet Allocation, 2018 Iowa State University

#### An Analysis Of Classroom Collusion Using Latent Dirichlet Allocation, Charles B. Shrader, Sue P. Ravenscroft, Jeffrey Kaufmann

*Management Conference Papers, Posters and Proceedings*

In this study, we use Latent Dirichlet Allocation to explore the reflections of students who faced a demanding classroom challenge, to which some responded by colluding. Our five-topic LDA solution describes the cheating event in terms of the nature of the course assignment itself, teams as a resource and support mechanism, the repercussions of cheating, and differences between majors or course tracks. The most relevant topics were the differences between the tracks and the repercussions of cheating. Teams and teammates also play a large role in the students’ reflections. We conclude with the implications of these topics in future research.

The Impact Of Sample Size In Cross-Classified Multiple Membership Multilevel Models, 2018 Chungnam National University

#### The Impact Of Sample Size In Cross-Classified Multiple Membership Multilevel Models, Hyewon Chung, Jiseon Kim, Ryoungsun Park, Hyeonjeong Jean

*Journal of Modern Applied Statistical Methods*

A simulation study was conducted to examine parameter recovery in a cross-classified multiple membership multilevel model. No substantial relative bias was identified for the fixed effect or level-one variance component estimates. However, the level-two cross-classification multiple membership factor variance components were substantially biased with relatively fewer groups.

Bias Assessment And Reduction In Kernel Smoothing, 2018 The University of Western Ontario

#### Bias Assessment And Reduction In Kernel Smoothing, Wenkai Ma

*Electronic Thesis and Dissertation Repository*

When performing local polynomial regression (LPR) with kernel smoothing, the choice of the smoothing parameter, or bandwidth, is critical. The performance of the method is often evaluated using the Mean Square Error (MSE). Bias and variance are two components of MSE. Kernel methods are known to exhibit varying degrees of bias. Boundary effects and data sparsity issues are two potential problems to watch for. There is a need for a tool to visually assess the potential bias when applying kernel smooths to a given scatterplot of data. In this dissertation, we propose pointwise confidence intervals for bias and demonstrate a ...

Using Cyclical Components To Improve The Forecasts Of The Stock Market And Macroeconomic Variables, 2018 Curtin University Malaysia

#### Using Cyclical Components To Improve The Forecasts Of The Stock Market And Macroeconomic Variables, Kenneth R. Szulczyk, Shibley Sadique

*Journal of Modern Applied Statistical Methods*

Economic variables such as stock market indices, interest rates, and national output measures contain cyclical components. Forecasting methods excluding these cyclical components yield inaccurate out-of-sample forecasts. Accordingly, a three-stage procedure is developed to estimate a vector autoregression (VAR) with cyclical components. A Monte Carlo simulation shows the procedure estimates the parameters accurately. Subsequently, a VAR with cyclical components improves the root-mean-square error of out-of-sample forecasts by 50% for a stock market model with macroeconomic variables.

Comparison Of Multiple Imputation Methods For Categorical Survey Items With High Missing Rates: Application To The Family Life, Activity, Sun, Health And Eating (Flashe) Study, 2018 National Cancer Institute

#### Comparison Of Multiple Imputation Methods For Categorical Survey Items With High Missing Rates: Application To The Family Life, Activity, Sun, Health And Eating (Flashe) Study, Benmei Liu, Erin Hennessy, April Oh, Laura A. Dwyer, Linda Nebeling

*Journal of Modern Applied Statistical Methods*

Two multiple imputation methods, the Sequential Regression Multivariate Imputation Algorithm and the Cox-Lannacchione Weighted Sequential Hotdeck, were examined and compared to impute highly missing categorical variables from the Family Life, Activity, Sun, Health and Eating (FLASHE) study. This paper describes the imputation approaches and results from the study.