Applied Statistics Commons^™

A Random Forests Approach To Assess Determinants Of Central Bank Independence, Maddalena Cavicchioli, Angeliki Papana, Ariadni Papana Dagiasis, Barbara Pistoresi

Journal of Modern Applied Statistical Methods

A non-parametric efficient statistical method, Random Forests, is implemented for the selection of the determinants of Central Bank Independence (CBI) among a large database of economic, political, and institutional variables for OECD countries. It permits ranking all the determinants based on their importance in respect to the CBI and does not impose a priori assumptions on potential nonlinear relationships in the data. Collinearity issues are resolved, because correlated variables can be simultaneously considered.

Maximum Likelihood Estimation For The Generalized Pareto Distribution And Goodness-Of-Fit Test With Censored Data, Minh H. Pham, Chris Tsokos, Bong-Jin Choi

Journal of Modern Applied Statistical Methods

The generalized Pareto distribution (GPD) is a flexible parametric model commonly used in financial modeling. Maximum likelihood estimation (MLE) of the GPD was proposed by Grimshaw (1993). Maximum likelihood estimation of the GPD for censored data is developed, and a goodness-of-fit test is constructed to verify an MLE algorithm in R and to support the model-validation step. The algorithms were composed in R. Grimshaw’s algorithm outperforms functions available in the R package ‘gPdtest’. A simulation study showed the MLE method for censored data and the goodness-of-fit test are both reliable.

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), 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, 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, 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.

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 …

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.

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 = X₁ − X₂, which in turn is related to making inferences about P(X₁ < X₂).

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, 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.

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.

Sustainable Energy Governance In South Tyrol (Italy): A Probabilistic Bipartite Network Model, Jessica Belest, Laura Secco, Elena Pisani, Alberto Caimo

Articles

At the national scale, almost all of the European countries have already achieved energy transition targets, while at the regional and local scales, there is still some potential to further push sustainable energy transitions. Regions and localities have the support of political, social, and economic actors who make decisions for meeting existing social, environmental and economic needs recognising local specificities.

These actors compose the sustainable energy governance that is fundamental to effectively plan and manage energy resources. In collaborative relationships, these actors share, save, and protect several kinds of resources, thereby making energy transitions deeper and more effective.

This research …

Step Away From Stepwise, Gary N. Smith

Pomona Economics

Stepwise regression is a popular data-mining tool that uses statistical significance to select the explanatory variables to be used in a multiple-regression model. A fundamental problem with stepwise regression is that some real explanatory variables that have causal effects on the dependent variable may happen to not be statistically significant, while nuisance variables may be coincidentally significant. As a result, the model may fit the data well in-sample, but do poorly out-of-sample. Many Big-Data researchers believe that, the larger the number of possible explanatory variables, the more useful is stepwise regression for selecting explanatory variables. The reality is that stepwise …

Be Wary Of Black-Box Trading Algorithms, Gary N. Smith

Pomona Economics

Black-box algorithms now account for nearly a third of all U. S. stock trades. It is a mistake to think that these algorithms possess superhuman intelligence. In reality, computers do not have the common sense and wisdom that humans have accumulated by living. Trading algorithms are particularly dangerous because they are so efficient at discovering statistical patterns—but so utterly useless in judging whether the discovered patterns are meaningful.

On Cluster Robust Models, José Bayoán Santiago Calderón

CGU Theses & Dissertations

Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches …

Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall

Numeracy

We discuss the connection between the numeracy and social justice movements both in historical context and in its modern incarnation. The intersection between numeracy and social justice encompasses a wide variety of disciplines and quantitative topics, but within that variety there are important commonalities. We examine the importance of sound quantitative measures for understanding social issues and the necessity of interdisciplinary collaboration in this work. Particular reference is made to the papers in the first part of the Numeracy special collection on social justice, which appear in this issue.

Genome-Wide Systems Genetics Of Alcohol Consumption And Dependence, Kristin Mignogna

Theses and Dissertations

Widely effective treatment for alcohol use disorder is not yet available, because the exact biological mechanisms that underlie this disorder are not completely understood. One way to gain a better understanding of these mechanisms is to examine the genetic frameworks that contribute to the risk for developing this disorder. This dissertation examines genetic association data in combination with gene expression networks in the brain to identify functional groups of genes associated with alcohol consumption and dependence.

The first study took advantage of the behavioral complexity of human samples, and experimental capabilities provided by mouse models, by co-analyzing gene expression networks …

Does It Take Three To Dance The Tango? Organizational Design, Triadic Structures And Boundary Spanning Across Subunits, Stefano Tasselli, Alberto Caimo

Articles

In this paper, we investigate the processes of boundary spanning across subunits within organizational networks. We hypothesize that patterns of advice across organizational subunits are explained by different triadic mechanisms depending on the organizational design of the intra-organizational network. In organizational networks characterized by flat hierarchy, we found triadic cyclic closure to be positively associated to boundary spanning across subunits; but when the network reflects an organizational structure with formal hierarchical differentiation among members, then we found triadic transitive closure to be associated to boundary spanning across subunits. We test these predictions in two empirical studies consisting of two organizational …

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, 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, 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.

Spatio-Temporal Reconstruction Of Remote Sensing Observations, Kamrul Khan

Graduate Theses and Dissertations

The USDA Forest Service aims to use satellite imagery for monitoring and predicting changes in forest conditions over time within the country. We specifically focus on a 230, 400 hectares region in north-central Wisconsin between 2003 - 2012. The auxiliary data collected from the satellite imagery of this region are relatively dense in space and time and can be used to efficiently predict how the forest condition changed over that decade. However, these records have a significant proportion of missing values due to weather conditions and system failures. To fill in these missing values, we build spaciotemporal models based on …

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.

An Introduction To Psychological Statistics, Garett C. Foster, David Lane, David Scott, Mikki Hebl, Rudy Guerra, Dan Osherson, Heidi Zimmer

Open Educational Resources Collection

This work has been superseded by Introduction to Statistics in the Psychological Sciences available from https://irl.umsl.edu/oer/25/.

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We are constantly bombarded by information, and finding a way to filter that information in an objective way is crucial to surviving this onslaught with your sanity intact. This is what statistics, and logic we use in it, enables us to do. Through the lens of statistics, we learn to find the signal hidden in the noise when it is there and to know when an apparent trend or pattern is really just randomness. The study of statistics involves math and relies …

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, 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.

Dealing With Sensitive Quantitative Variables: A Comparison Of Sampling Designs For The Procedure Of Gupta And Thornton, Carlos Narciso Bouza Herrera, Prayas Sharma

Journal of Modern Applied Statistical Methods

The use of randomized response procedures allows diminishing the number of non-responses and increasing the accuracy of the responses. A new sampling strategy is developed where the reports are scrambled using the procedure of Gupta and Thornton. The estimator of the mean as well as the errors are developed for the Rao-Hartley-Cochran and Ranked Sets Sampling designs. The proposals are compared with the original model based on the use of simple random sampling.

Bayesian And Semi-Bayesian Estimation Of The Parameters Of Generalized Inverse Weibull Distribution, Kamaljit Kaur, Kalpana K. Mahajan, Sangeeta Arora

Journal of Modern Applied Statistical Methods

Bayesian and semi-Bayesian estimators of parameters of the generalized inverse Weibull distribution are obtained using Jeffreys’ prior and informative prior under specific assumptions of loss function. Using simulation, the relative efficiency of the proposed estimators is obtained under different set-ups. A real life example is also given.

Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels

SMU Data Science Review

In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …