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Contributions To Statistical Testing, Prediction, And Modeling, John C. Pesko 2017 University of New Mexico

Contributions To Statistical Testing, Prediction, And Modeling, John C. Pesko

Mathematics & Statistics ETDs

1. "Parametric Bootstrap (PB) and Objective Bayesian (OB) Testing with Applications to Heteroscedastic ANOVA": For one-way heteroscedastic ANOVA, we show a close relationship between the PB and OB approaches to significance testing, demonstrating the conditions for which the two approaches are equivalent. Using a simulation study, PB and OB performance is compared to a test based on the predictive distribution as well as the unweighted test of Akritas & Papadatos (2004). We extend this work to the RCBD with subsampling model, and prove a repeated sampling property and large sample property for general OB significance testing.

2. "Early Identification of Binswanger ...


Session D-5: Informal Comparative Inference: What Is It?, Karen Togliatti 2017 Illinois Mathematics and Science Academy

Session D-5: Informal Comparative Inference: What Is It?, Karen Togliatti

Professional Learning Day

Come and experience a hands-on task that has middle-school students grapple with informal inferential reasoning. Three key principles of informal inference – data as evidence, probabilistic language, and generalizing ‘beyond the data’ will be discussed as students build and analyze distributions to answer the question, “Does hand dominance play a role in throwing accuracy?” Connections to the CCSSM statistics standards for middle-school will be highlighted.


Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. van der Laan, Maya L. Petersen 2017 Department of Biostatistics, Harvard T.H. Chan School of Public Heath

Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen

Laura B. Balzer

WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of "test and treat" strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral ...


Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. van der Laan, Maya L. Petersen 2017 Department of Biostatistics, Harvard T.H. Chan School of Public Heath

Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen

U.C. Berkeley Division of Biostatistics Working Paper Series

WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of "test and treat" strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral ...


Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy 2017 University of Missouri

Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy

Statistics Publications

In dynamic linear models (DLMs) with unknown fixed parameters, a standard Markov chain Monte Carlo (MCMC) sampling strategy is to alternate sampling of latent states conditional on fixed parameters and sampling of fixed parameters conditional on latent states. In some regions of the parameter space, this standard data augmentation (DA) algorithm can be inefficient. To improve efficiency, we apply the interweaving strategies of Yu and Meng to DLMs. For this, we introduce three novel alternative DAs for DLMs: the scaled errors, wrongly scaled errors, and wrongly scaled disturbances. With the latent states and the less well known scaled disturbances, this ...


An Approximate Bayesian Inference On Propensity Score Estimation Under Unit Nonresponse, Hejian Sang, Jae Kwang Kim 2017 Iowa State University

An Approximate Bayesian Inference On Propensity Score Estimation Under Unit Nonresponse, Hejian Sang, Jae Kwang Kim

Statistics Preprints

Nonresponse weighting adjustment using the response propensity score is a popular tool for handling unit nonresponse. Statistical inference after the non- response weighting adjustment is complicated because the effect of estimating the propensity model parameter needs to be incorporated. In this paper, we propose an approximate Bayesian approach to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome variable. The proposed Bayesian method is cal- ibrated to the frequentist inference in that the credible region obtained from the posterior distribution asymptotically matches to the frequentist confidence interval obtained from the Taylor ...


It's All About Balance: Propensity Score Matching In The Context Of Complex Survey Data, David Lenis, Trang Q. ;Nguyen, Nian Dong, Elizabeth A. Stuart 2017 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

It's All About Balance: Propensity Score Matching In The Context Of Complex Survey Data, David Lenis, Trang Q. ;Nguyen, Nian Dong, Elizabeth A. Stuart

Johns Hopkins University, Dept. of Biostatistics Working Papers

Many research studies aim to draw causal inferences using data from large, nationally representative survey samples, and many of these studies use propensity score matching to make those causal inferences as rigorous as possible given the non-experimental nature of the data. However, very few applied studies are careful about incorporating the survey design with the propensity score analysis, which may mean that the results don’t generate population inferences. This may be because few methodological studies examine how to best combine these methods. Furthermore, even fewer of the methodological studies incorporate different non-response mechanisms in their analysis. This study examines ...


Direct Nitrous Oxide Emissions In Mediterranean Climate Cropping Systems: Emission Factors Based On A Meta-Analysis Of Available Measurement Data, Maria L. Cayuela, Eduardo Aguilera, Alberto Sanz-Cobena, Dean C. Adams, Diego Abalos, Louise Barton, Rebecca Ryals, Whendee L. Silver, Marta A. Alfaro, Valentini A. Pappa, Pete Smith, Josette Garnier, Gilles Billen, Lex Bouwman, Alberte Bondeau, Luis Lassaletta 2017 Campus Universitario de Espinardo

Direct Nitrous Oxide Emissions In Mediterranean Climate Cropping Systems: Emission Factors Based On A Meta-Analysis Of Available Measurement Data, Maria L. Cayuela, Eduardo Aguilera, Alberto Sanz-Cobena, Dean C. Adams, Diego Abalos, Louise Barton, Rebecca Ryals, Whendee L. Silver, Marta A. Alfaro, Valentini A. Pappa, Pete Smith, Josette Garnier, Gilles Billen, Lex Bouwman, Alberte Bondeau, Luis Lassaletta

Ecology, Evolution and Organismal Biology Publications

Many recent reviews and meta‐analyses of N2O emissions do not include data from Mediterranean studies. In this paper we present a meta‐analysis of the N2O emissions from Mediterranean cropping systems, and propose a more robust and reliable regional emission factor (EF) for N2O, distinguishing the effects of water management, crop type, and fertilizer management. The average overall EF for Mediterranean agriculture (EFMed) is 0.5%, which is substantially lower than the IPCC default value of 1%. Soil properties had no significant effect on EFs for N2O. Increasing the nitrogen fertilizer rate led to higher EFs; when N was ...


Variance Prior Specification For A Basket Trial Design Using Bayesian Hierarchical Modeling, Kristen Cunanan, Alexia Iasonos, Ronglai Shen, Mithat Gonen 2017 Memorial Sloan Kettering Cancer Center

Variance Prior Specification For A Basket Trial Design Using Bayesian Hierarchical Modeling, Kristen Cunanan, Alexia Iasonos, Ronglai Shen, Mithat Gonen

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is ...


Optimized Variable Selection Via Repeated Data Splitting, Marinela Capanu, Colin B. Begg, Mithat Gonen 2017 Memorial Sloan-Kettering Cancer Center

Optimized Variable Selection Via Repeated Data Splitting, Marinela Capanu, Colin B. Begg, Mithat Gonen

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

We introduce a new variable selection procedure that repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model. Simulation results show that the proposed variable selection technique enjoys superior performance compared to candidate methods, being amongst those with the lowest inclusion of noisy predictors while having the highest power to detect the correct model and being unaffected by correlations among the predictors. We illustrate the methods by applying them to a cohort of patients undergoing hepatectomy ...


Informational Index And Its Applications In High Dimensional Data, Qingcong Yuan 2017 University of Kentucky

Informational Index And Its Applications In High Dimensional Data, Qingcong Yuan

Theses and Dissertations--Statistics

We introduce a new class of measures for testing independence between two random vectors, which uses expected difference of conditional and marginal characteristic functions. By choosing a particular weight function in the class, we propose a new index for measuring independence and study its property. Two empirical versions are developed, their properties, asymptotics, connection with existing measures and applications are discussed. Implementation and Monte Carlo results are also presented.

We propose a two-stage sufficient variable selections method based on the new index to deal with large p small n data. The method does not require model specification and especially focuses ...


Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu 2017 University of Kentucky

Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu

Theses and Dissertations--Statistics

Firstly, we reviewed some popular nonparameteric regression methods during the past several decades. Then we extended the compound estimation (Charnigo and Srinivasan [2011]) to adapt random design points and heteroskedasticity and proposed a modified Cp criteria for tuning parameter selection. Moreover, we developed a DCp criteria for tuning paramter selection problem in general nonparametric derivative estimation. This extends GCp criteria in Charnigo, Hall and Srinivasan [2011] with random design points and heteroskedasticity. Next, we proposed a change point detection method via compound estimation for both fixed design and random design case, the adaptation of heteroskedasticity was considered for the method ...


Inference Using Bhattacharyya Distance To Model Interaction Effects When The Number Of Predictors Far Exceeds The Sample Size, Sarah A. Janse 2017 University of Kentucky

Inference Using Bhattacharyya Distance To Model Interaction Effects When The Number Of Predictors Far Exceeds The Sample Size, Sarah A. Janse

Theses and Dissertations--Statistics

In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that ...


Teaching Size And Power Properties Of Hypothesis Tests Through Simulations, Suleyman Taspinar, Osman Dogan 2017 Graduate Center, City University of New York

Teaching Size And Power Properties Of Hypothesis Tests Through Simulations, Suleyman Taspinar, Osman Dogan

Publications and Research

In this study, we review the graphical methods suggested in Davidson and MacKinnon (Davidson, Russell, and James G. MacKinnon. 1998. “Graphical Methods for Investigating the Size and Power of Hypothesis Tests.” The Manchester School 66 (1): 1–26.) that can be used to investigate size and power properties of hypothesis tests for undergraduate and graduate econometrics courses. These methods can be used to assess finite sample properties of various hypothesis tests through simulation studies. In addition, these methods can be effectively used in classrooms to reinforce students’ understanding of basic hypothesis testing concepts such as Type I error, Type II ...


What’S Brewing? A Statistics Education Discovery Project, Marla A. Sole, Sharon L. Weinberg 2017 CUNY Guttman Community College

What’S Brewing? A Statistics Education Discovery Project, Marla A. Sole, Sharon L. Weinberg

Publications and Research

We believe that students learn best, are actively engaged, and are genuinely interested when working on real-world problems. This can be done by giving students the opportunity to work collaboratively on projects that investigate authentic, familiar problems. This article shares one such project that was used in an introductory statistics course. We describe the steps taken to investigate why customers are charged more for iced coffee than hot coffee, which included collecting data and using descriptive and inferential statistical analysis. Interspersed throughout the article, we describe strategies that can help teachers implement the project and scaffold material to assist students ...


Approximate Bayesian Computation In Forensic Science, Jessie H. Hendricks 2017 South Dakota State University

Approximate Bayesian Computation In Forensic Science, Jessie H. Hendricks

The Journal of Undergraduate Research

Forensic evidence is often an important factor in criminal investigations. Analyzing evidence in an objective way involves the use of statistics. However, many evidence types (i.e., glass fragments, fingerprints, shoe impressions) are very complex. This makes the use of statistical methods, such as model selection in Bayesian inference, extremely difficult.

Approximate Bayesian Computation is an algorithmic method in Bayesian analysis that can be used for model selection. It is especially useful because it can be used to assign a Bayes Factor without the need to directly evaluate the exact likelihood function - a difficult task for complex data. Several criticisms ...


On The Equivalence Between Bayesian And Frequentist Nonparametric Hypothesis Testing, Qiuchen Hai 2017 Michigan Technological University

On The Equivalence Between Bayesian And Frequentist Nonparametric Hypothesis Testing, Qiuchen Hai

Dissertations, Master's Theses and Master's Reports

Testing of hypotheses about the population parameter is one of the most fundamental tasks in the empirical sciences and is often conducted by using parametric tests (e.g., the t-test and F-test), in which they assume that the samples are from populations that are normally distributed. When the normality assumption is violated, nonparametric tests are employed as alternatives for making statistical inference. In recent years, the Bayesian versions of parametric tests have been well studied in the literature, whereas in contrast, the Bayesian versions of nonparametric tests are quite scant (for exception, Yuan and Johnson (2008) ) in the literature, mainly ...


Bayesian Exponential Random Graph Modelling Of Interhospital Patient Referral Networks, Alberto Caimo, Francesca Pallotti, Alessandro Lomi 2017 Dublin Institute of Technology

Bayesian Exponential Random Graph Modelling Of Interhospital Patient Referral Networks, Alberto Caimo, Francesca Pallotti, Alessandro Lomi

Articles

Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system ...


Comparing The Structural Components Variance Estimator And U-Statistics Variance Estimator When Assessing The Difference Between Correlated Aucs With Finite Samples, Anna L. Bosse 2017 Virginia Commonwealth University

Comparing The Structural Components Variance Estimator And U-Statistics Variance Estimator When Assessing The Difference Between Correlated Aucs With Finite Samples, Anna L. Bosse

Theses and Dissertations

Introduction: The structural components variance estimator proposed by DeLong et al. (1988) is a popular approach used when comparing two correlated AUCs. However, this variance estimator is biased and could be problematic with small sample sizes.

Methods: A U-statistics based variance estimator approach is presented and compared with the structural components variance estimator through a large-scale simulation study under different finite-sample size configurations.

Results: The U-statistics variance estimator was unbiased for the true variance of the difference between correlated AUCs regardless of the sample size and had lower RMSE than the structural components variance estimator, providing better type 1 error ...


A Semiparametric Inference To Regression Analysis With Missing Covariates In Survey Data, Shu Yang, Jae Kwang Kim 2017 North Carolina State University

A Semiparametric Inference To Regression Analysis With Missing Covariates In Survey Data, Shu Yang, Jae Kwang Kim

Statistics Publications

Parameter estimation in parametric regression models with missing covariates is considered under a survey sampling setup. Under missingness at random, a semiparametric maximum likelihood approach is proposed which requires no parametric specification of the marginal covariate distribution. By drawing from the von Mises calculus and V-Statistics theory, we obtain an asymptotic linear representation of the semiparametric maximum likelihood estimator (SMLE) of the regression parameters, which allows for a consistent estimator of asymptotic variance. An EM algorithm for computation is then developed to implement the proposed method using fractional imputation. Simulation results suggest that the SMLE method is robust, whereas the ...


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