Physical Sciences and Mathematics Commons^™

Beta Binomial Regression, Joseph M. Hilbe

Joseph M Hilbe

Monograph on how to construct, interpret and evaluate beta, beta binomial, and zero inflated beta-binomial regression models. Stata and R code used for examples.

An L-Moment Based Characterization Of The Family Of Dagum Distributions, Mohan D. Pant, Todd C. Headrick

Mohan Dev Pant

This paper introduces a method for simulating univariate and multivariate Dagum distributions through the method of L-moments and L-correlation. A method is developed for characterizing non-normal Dagum distributions with controlled degrees of L-skew, L-kurtosis, and L-correlations. The procedure can be applied in a variety of contexts such as statistical modeling (e.g., income distribution, personal wealth distributions, etc.) and Monte Carlo or simulation studies. Numerical examples are provided to demonstrate that -moment-based Dagum distributions are superior to their conventional moment-based analogs in terms of estimation and distribution fitting. Evaluation of the proposed method also demonstrates that the estimates of L-skew, L-kurtosis, …

Bayesian Nonparametric Reliability Analysis For A Railway System At Component Level, Payam Mokhtarian, Mohammad-Reza Namazi-Rad, Ho Tin Kin, Thomas Suesse

Mohammad-Reza NAMAZI-RAD Ph.D.

Railway system is a typical large-scale complex system with interconnected sub-systems which contain numerous components. System reliability is retained through appropriate maintenance measures and cost-effective asset management requires accurate estimation of reliability at the lowest level. However, real-life reliability data at component level of a railway system is not always available in practice, let alone complete. The component lifetime distributions from the manufacturers are often obscured and complicated by the actual usage and working environments. Reliability analysis thus calls for a suitable methodology to estimate a component lifetime under the conditions of a lack of failure data and unknown and/or …

Hamamatsu Flash4.0 Scmos Exposure Time Series, George Mcnamara

George McNamara

Hamamatsu FLASH4.0 scientific cMOS camera exposure time series are pairs of images of:

1 millisecond (00,001ms series)

10 millisecond (00,010ms series)

100 millisecond (00,100ms series)

1,000 millisecond (01,000ms series)

4,000 millisecond (04,000ms series)

10,000 millisecond (10,000ms series)

I also included:

* difference images (exposure 2 minus exposure 1 plus 100 intensity values).

* a series of eleven 1 second (1,000 ms) exposure time images in a multi-plane TIFF file (different images than the pair of 1,000ms images above).

* Stack Arithmetic: Median, Average, Minimum, Maximum, of the eleven plane series (Stack Arithmetic is a MetaMorph command).

These images were acquired …

諸外国における最新のデータエディティング事情～混淆正規分布モデルによる多変量外れ値検出法の検証～(高橋将宜、選択的エディティング、セレクティブエディティング), Masayoshi Takahashi

Masayoshi Takahashi

No abstract provided.

Gee-2 R Data Files, Joseph Hilbe

Joseph M Hilbe

Generalized Estimating Equations, 2nd edition Publsihed: 10 December, 2012 R Data Files

Gee-2 R Scripts And Functions, Joseph Hilbe

Joseph M Hilbe

Generalized Estimating Equations, 2nd edition Published: 10 December, 2012 R scripts and functions

Caimans - Semantic Platform For Advance Content Mining (Sketch Wp), Salvo Reina

Salvo Reina

A middleware SW platform was created for automatic classification of textual contents. The worksheet of requirements and the original flow-sketchs are published.

Errata And Comments For: Generalized Estimating Equations, 2nd Ed, Joseph M. Hilbe, James W. Hardin

Joseph M Hilbe

Errata and Comments for Hardin & Hilbe, Generalized Estimating Equations, 2nd ed (published 10 Dec, 2012)

A Method For Simulating Burr Type Iii And Type Xii Distributions Through L-Moments And L-Correlations, Mohan D. Pant, Todd C. Headrick

Mohan Dev Pant

This paper derives the Burr Type III and Type XII family of distributions in the contexts of univariate L-moments and the L-correlations. Included is the development of a procedure for specifying nonnormal distributions with controlled degrees of L-skew, L-kurtosis, and L-correlations. The procedure can be applied in a variety of settings such as statistical modeling (e.g., forestry, fracture roughness, life testing, operational risk, etc.) and Monte Carlo or simulation studies. Numerical examples are provided to demonstrate that L-moment-based Burr distributions are superior to their conventional moment-based analogs in terms of estimation and distribution fitting. Evaluation of the proposed procedure also …

A Study Of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Modeling Of Nonstationary Time Series Data With Time-Dependent Spectra, Josue G. Martinez, Kirsten M. Bohn, Raymond J. Carroll, Jeffrey S. Morris

Jeffrey S. Morris

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to …

Global Quantitative Assessment Of The Colorectal Polyp Burden In Familial Adenomatous Polyposis Using A Web-Based Tool, Patrick M. Lynch, Jeffrey S. Morris, William A. Ross, Miguel A. Rodriguez-Bigas, Juan Posadas, Rossa Khalaf, Diane M. Weber, Valerie O. Sepeda, Bernard Levin, Imad Shureiqi

Jeffrey S. Morris

Background: Accurate measures of the total polyp burden in familial adenomatous polyposis (FAP) are lacking. Current assessment tools include polyp quantitation in limited-field photographs and qualitative total colorectal polyp burden by video.

Objective: To develop global quantitative tools of the FAP colorectal adenoma burden.

Design: A single-arm, phase II trial.

Patients: Twenty-seven patients with FAP.

Intervention: Treatment with celecoxib for 6 months, with before-treatment and after-treatment videos posted to an intranet with an interactive site for scoring.

Main Outcome Measurements: Global adenoma counts and sizes (grouped into categories: less than 2 mm, 2-4 mm, and greater than 4 mm) were …

Some Exponential Ratio-Product Type Estimators Using Information On Auxiliary Attributes Under Second Order Approximation, Prayas Sharma, Rajesh Singh, Hemant Kumar Verma, Amir Sanaullah

PRAYAS SHARMA

No abstract provided.

Study Of Some Improved Ratio Type Estimators Using Information On Auxiliary Attributes Under Second Order Approximation, Prayas Sharma, Rajesh Singh, Jong-Min Kim

PRAYAS SHARMA

No abstract provided.

Improving The Retrieval Of Water Inherent Optical Properties In Noisy Hyperspectral Data Through Statistical Modeling, David B. Gillis, Jeffrey H. Bowles, Wesley J. Moses

Wesley Moses

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in the retrieved IOPs. For LUT-based retrievals that rely on spectrum matching, the particular metric used for spectral comparisons has a significant effect on the accuracy of the results, especially in the presence of noise …

Adaptive Randomization To Improve Utility-Based Dose-Finding With Bivariate Ordinal Outcomes., Peter F. Thall

Peter F. Thall

No abstract provided.

Using Joint Utilities Of The Times To Response And Toxicity To Adaptively Optimize Schedule-Dose Regimes, Peter F. Thall

Peter F. Thall

No abstract provided.

Sas Macro: Kappa Statistic For Clustered Matched-Pair Data, Zhao Yang

Zhao (Tony) Yang, Ph.D.

The SAS macro was developed to calculate the kappa statistic for the clustered matched-pair data.

Dose-Response And Finding In Phase Ii Clinical Studies — Mcp-Mod Methodologies, Zhao Yang

Zhao (Tony) Yang, Ph.D.

This presentation give an overall introduction to the MCP-Mod methodology with detailed step-by-step demonstration.

Phase I Design For Multiple Treatment Schedules, Nolan A. Wages

Nolan A. Wages

No abstract provided.

Early-Phase Dose-Finding Design For Oncology Trials Of Molecularly Targeted Agents, Nolan A. Wages

Nolan A. Wages

No abstract provided.

Bayesian Inferences For Beta Semiparametric Mixed Models To Analyze Longitudinal Neuroimaging Data, Xiaofeng Wang, Yingxing Li

Xiaofeng Wang

Diffusion tensor imaging (DTI) is a quantitative magnetic resonance imaging technique that measures the three-dimensional diffusion of water molecules within tissue through the application of multiple diffusion gradients. This technique is rapidly increasing in popularity for studying white matter properties and structural connectivity in the living human brain. The major measure derived from the DTI process is known as fractional anisotropy, a continuous measure restricted on the interval (0,1). Motivated from a DTI study of multiple sclerosis, we use a beta semiparametric mixed-effect regression model for the longitudinal neuroimaging data. This work extends the generalized additive model methodology with beta …

Bayesian Nonparametric Regression And Density Estimation Using Integrated Nested Laplace Approximations, Xiaofeng Wang

Xiaofeng Wang

Integrated nested Laplace approximations (INLA) are a recently proposed approximate Bayesian approach to fit structured additive regression models with latent Gaussian field. INLA method, as an alternative to Markov chain Monte Carlo techniques, provides accurate approximations to estimate posterior marginals and avoid time-consuming sampling. We show here that two classical nonparametric smoothing problems, nonparametric regression and density estimation, can be achieved using INLA. Simulated examples and \texttt{R} functions are demonstrated to illustrate the use of the methods. Some discussions on potential applications of INLA are made in the paper.

An L-Moment Based Characterization Of The Family Of Dagum Distributions, Mohan D. Pant, Todd C. Headrick

Todd Christopher Headrick

This paper introduces a method for simulating univariate and multivariate Dagum distributions through the method of 𝐿-moments and 𝐿-correlations. A method is developed for characterizing non-normal Dagum distributions with controlled degrees of 𝐿-skew, 𝐿-kurtosis, and 𝐿-correlations. The procedure can be applied in a variety of contexts such as statistical modeling (e.g., income distribution, personal wealth distributions, etc.) and Monte Carlo or simulation studies. Numerical examples are provided to demonstrate that 𝐿-moment-based Dagum distributions are superior to their conventional moment-based analogs in terms of estimation and distribution fitting. Evaluation of the proposed method also demonstrates that the estimates of 𝐿-skew, 𝐿-kurtosis, …

Sberia: Set Based Gene Environment Interaction Test For Rare And Common Variants In Complex Diseases, Shuo Jiao, Li Hsu, Stéphane Bézieau, Hermann Brenner, Andrew T. Chan, Jenny Chang-Claude, Loic Le Marchand, Mathieu Lemire, Polly A. Newcomb, Martha L. Slattery, Ulrike Peters

Shuo Jiao

Identification of gene-environment interaction (GxE) is important in understanding the etiology of complex diseases. However, partially due to the lack of power, there have been very few replicated GxE findings compared to the success in marginal association studies. The existing GxE testing methods mainly focus on improving the power for individual markers. In this paper, we took a different strategy and proposed a Set Based gene EnviRonment InterAction test (SBERIA), which can improve the power by reducing the multiple testing burdens and aggregating signals within a set. The major challenge of the signal aggregation within a set is how to …

Mixtures Of Receiver Operating Characteristic Curves, Mithat Gonen

Mithat Gönen

Rationale and Objectives: ROC curves are ubiquitous in the analysis of imaging metrics as markers of both diagnosis and prognosis. While empirical estimation of ROC curves remains the most popular method, there are several reasons to consider smooth estimates based on a parametric model.

Materials and Methods: A mixture model is considered for modeling the distribution of the marker in the diseased population motivated by the biological observation that here is more heterogeneity in the diseased population than there is in the normal one. It is shown that this model results in an analytically tractable ROC curve which is itself …

Penalized Regression Procedures For Variable Selection In The Potential Outcomes Framework, Debashis Ghosh, Yeying Zhu, Donna L. Coffman

Debashis Ghosh

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple `impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model, and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data and imputation …

A Data-Adaptive Strategy For Inverse Weighted Estimation Of Causal Effects, Yeying Zhu, Debashis Ghosh, Bhramar Mukherjee, Nandita Mitra

Debashis Ghosh

In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, the average treatment effect is often estimated by using propensity scores. In this article, we focus on the use of inverse probability weighted (IPW) estimation methods. Typically, propensity scores are estimated by logistic regression. More recent suggestions have been to employ nonparametric classification algorithms from machine learning. In this article, we …

Using Methods From The Data-Mining And Machine-Learning Literature For Disease Classification And Prediction: A Case Study Examining Classification Of Heart Failure Subtypes, Peter C. Austin

Peter Austin

OBJECTIVE: Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine-learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines.

STUDY DESIGN AND SETTING: We compared the performance of these classification methods with that of conventional classification trees to classify patients with heart failure (HF) …

Predictive Accuracy Of Risk Factors And Markers: A Simulation Study Of The Effect Of Novel Markers On Different Performance Measures For Logistic Regression Models, Peter C. Austin

Peter Austin

The change in c-statistic is frequently used to summarize the change in predictive accuracy when a novel risk factor is added to an existing logistic regression model. We explored the relationship between the absolute change in the c-statistic, Brier score, generalized R(2) , and the discrimination slope when a risk factor was added to an existing model in an extensive set of Monte Carlo simulations. The increase in model accuracy due to the inclusion of a novel marker was proportional to both the prevalence of the marker and to the odds ratio relating the marker to the outcome but inversely …