Functional Regression, 2015 The University of Texas

#### Functional Regression, Jeffrey S. Morris

*Jeffrey S. Morris*

Functional data analysis (FDA) involves the analysis of data whose ideal units of observation are functions defined on some continuous domain, and the observed data consist of a sample of functions taken from some population, sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the development of this field, which has accelerated in the past 10 years to become one of the fastest growing areas of statistics, fueled by the growing number of applications yielding this type of data. One unique characteristic of FDA is the need to combine information both across and within functions, which Ramsay ...

Mcd - Stata Commands, 2014 SelectedWorks

#### Mcd - Stata Commands, Joseph M. Hilbe

*Joseph M Hilbe*

Stata commands and affiliated files for examples in book. Text file explanation of command names is included. 103 files in total

A Novel Targeted Learning Method For Quantitative Trait Loci Mapping, 2014 COBRA

#### A Novel Targeted Learning Method For Quantitative Trait Loci Mapping, Hui Wang, Zhongyang Zhang, Sherri Rose, Mark J. Van Der Laan

*U.C. Berkeley Division of Biostatistics Working Paper Series*

We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum likelihood estimation. In contrast with univariate regression and interval mapping methods, our model requires fewer assumptions and also accommodates various machine learning algorithms. Estimation is performed with targeted maximum likelihood learning methods. We demonstrate our semiparametric targeted learning approach in a simulation study and a well-studied barley dataset.

A Simple Regression-Based Approach To Account For Survival Bias In Birth Outcomes Research, 2014 COBRA

#### A Simple Regression-Based Approach To Account For Survival Bias In Birth Outcomes Research, Eric J. Tchetgen Tchetgen, Kelesitse Phiri, Roger Shapiro

*Harvard University Biostatistics Working Paper Series*

No abstract provided.

A Note On The Control Function Approach With An Instrumental Variable And A Binary Outcome, 2014 COBRA

#### A Note On The Control Function Approach With An Instrumental Variable And A Binary Outcome, Eric Tchetgen Tchetgen

*Harvard University Biostatistics Working Paper Series*

No abstract provided.

Modeling Count Data; Errata And Comments, 2014 SelectedWorks

#### Modeling Count Data; Errata And Comments, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data: Errata and Comments PDF. Will be updated on a continuing basis.

#### Entering The Era Of Data Science: Targeted Learning And The Integration Of Statistics And Computational Data Analysis, Mark J. Van Der Laan, Richard J.C.M. Starmans

*U.C. Berkeley Division of Biostatistics Working Paper Series*

This outlook article will appear in Advances in Statistics and it reviews the research of Dr. van der Laan's group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming to only rely on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment ...

Control Function Assisted Ipw Estimation With A Secondary Outcome In Case-Control Studies, 2014 COBRA

#### Control Function Assisted Ipw Estimation With A Secondary Outcome In Case-Control Studies, Tamar Sofer, Marilyn C. Cornelis, Peter Kraft, Eric J. Tchetgen Tchetgen

*Harvard University Biostatistics Working Paper Series*

No abstract provided.

Hilbe-Mcd-Cvs-Data, 2014 SelectedWorks

#### Hilbe-Mcd-Cvs-Data, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data, data files from book in CVS format

Mcd - 11 Stata Data Files, 2014 SelectedWorks

#### Mcd - 11 Stata Data Files, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data: 11 Stata files from book

Mcd - 11 R Data Files From Book, 2014 SelectedWorks

#### Mcd - 11 R Data Files From Book, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data: ZIP file with 11 R data files from book

Mcd - 11 Excel Data Files, 2014 SelectedWorks

#### Mcd - 11 Excel Data Files, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data - 11 Excel files for use with the book

Mcd-Description, 2014 SelectedWorks

#### Mcd-Description, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data - description of Data Files with examples using R, Stata and SAS

Mcd-Information-, 2014 SelectedWorks

#### Mcd-Information-, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data - Information about book and resources

What Is Higher Mathematics? Why Is It So Hard To Interpret? What Can Be Done?, 2014 University of North Florida

#### What Is Higher Mathematics? Why Is It So Hard To Interpret? What Can Be Done?, John Tabak

*Journal of Interpretation*

Courses and seminars in higher mathematics are some of the most challenging assignments faced by academic interpreters. Difficulties interpreting higher mathematics can adversely impact the academic and professional aspirations of deaf mathematics students and professionals. This paper discusses the nature of higher mathematics with the goal of identifying what distinguishes higher mathematics from other subjects; it then reviews the history of attempts to sign/interpret higher mathematics with particular attention to current challenges associated with expressing higher mathematics in sign. The final part of the paper discusses strategies for more effectively expressing higher mathematics in American Sign Language.

Meta-Analysis Of Type I Error Rates For Detecting Differential Item Functioning With Logistic Regression And Mantel-Haenszel In Monte Carlo Studies, 2014 SelectedWorks

#### Meta-Analysis Of Type I Error Rates For Detecting Differential Item Functioning With Logistic Regression And Mantel-Haenszel In Monte Carlo Studies, Eva Van De Water Ph. D.

*Eva Van De Water*

Differential item functioning (DIF) occurs when individuals from different groups who have equal levels of a latent trait fail to earn commensurate scores on a testing instrument. Type I error occurs when DIF-detection methods result in unbiased items being excluded from the test while a Type II error occurs when biased items remain on the test after DIF-detection methods have been employed. Both errors create potential issues of injustice amongst examinees and can result in costly and protracted legal action. The purpose of this research was to evaluate two methods for detecting DIF: logistic regression (LR) and Mantel-Haenszel (MH).

To ...

Mdc-Stata-Code, 2014 SelectedWorks

#### Mdc-Stata-Code, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data, Stata code in book for use

Mdc-Sas-Code, 2014 SelectedWorks

#### Mdc-Sas-Code, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data, SAS files for download and use

Mcd-Figures-Code, 2014 SelectedWorks

#### Mcd-Figures-Code, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data, code for Figures in book - R and Stata

Mdc-R-Code, 2014 SelectedWorks

#### Mdc-R-Code, Joseph M. Hilbe

*Joseph M Hilbe*

Modeling Count Data: R code for download and use.