Physical Sciences and Mathematics Commons^™

Mcd-Description, Joseph M. Hilbe

Joseph M Hilbe

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

Mcd-Information-, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data - Information about book and resources

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 Stata Data Files, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data: 11 Stata files from book

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

Joseph M Hilbe

Modeling Count Data, data files from book in CVS format

Mcd Information, Joseph M. Hilbe

Joseph M Hilbe

Information on Modeling Count Data

Mcd Description Data Files: Stata-R-Sas-Excel, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data: Description of Data Files R, Stata, SAS examples

Mcd-Figures-Code, Joseph M. Hilbe

Joseph M Hilbe

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

Mdc-Sas-Code, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data, SAS files for download and use

Mcd-Data-Sas, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data, 11 SAS data files. SAS users

A General Framework For Uncertainty Propagation Based On Point Estimate Methods, René Schenkendorf

René Schenkendorf

A general framework to approach the challenge of uncertainty propagation in model based prognostics is presented in this work. It is shown how the so-called Point Estimate Meth- ods (PEMs) are ideally suited for this purpose because of the following reasons: 1) A credible propagation and represen- tation of Gaussian (normally distributed) uncertainty can be done with a minimum of computational effort for non-linear applications. 2) Also non-Gaussian uncertainties can be prop- agated by evaluating suitable transfer functions inherently. 3) Confidence intervals of simulation results can be derived which do not have to be symmetrically distributed around the mean value …

Measuring Gender Difference In Information Sharing Using Network Analysis: The Case Of The Austrian Interlocking Directorship Network In 2009, Carlo Drago, Livia Amidani Aliberti, Davide Carbonai

Carlo Drago

In recent literature a relevant problem has been the relationship between career/personal contact networks and different career paths. In addition the recent advances in social capital theory have shown the way in which networks impact on personal careers. In particular women’s careers appear to be negatively affected by the informational network structure. The main contribution of this work is to propose empirical evidence of this phenomenon by considering the gendered directorship network with relation to Austria and to show the structural differences by gender in the network. By using community detection techniques we have found various communities in which females …

Errata - Logistic Regression Models, Joseph Hilbe

Joseph M Hilbe

Errata for Logistic Regression Models, 4th Printing

The Modified R A Robust Measure Of Association For Time Series, Muhammad Irfan Malik

irfan.phdet24@iiu.edu.pk

Since times of Yule (1926), it is known that correlation between two time series can produce spurious results. Granger and Newbold (1974) see the roots of spurious correlation in non-stationarity of the time series. However the study of Granger, Hyung and Jeon (2001) prove that spurious correlation also exists in stationary time series. These facts make the correlation coefficient an unreliable measure of association. This paper proposes ‘Modified R’ as an alternate measure of association for the time series. The Modified R is robust to the type of stationarity and type of deterministic part in the time series. The performance …

Interpretation And Prediction Of A Logistic Model, Joseph M. Hilbe

Joseph M Hilbe

A basic overview of how to model and interpret a logistic regression model, as well as how to obtain the predicted probability or fit of the model and calculate its confidence intervals. R code used for all examples; some Stata is provided as a contrast.

Simulating Univariate And Multivariate Tukey G-And-H Distributions Based On The Method Of Percentiles, Tzu-Chun Kou, Todd C. Headrick

Todd Christopher Headrick

This paper derives closed-form solutions for the 𝑔-and-ℎ shape parameters associated with the Tukey family of distributions based on the method of percentiles (MOP). A proposed MOP univariate procedure is described and compared with the method of moments (MOM) in the context of distribution fitting and estimating skew and kurtosis functions. The MOP methodology is also extended from univariate to multivariate data generation. A procedure is described for simulating nonnormal distributions with specified Spearman correlations. The MOP procedure has an advantage over the MOM because it does not require numerical integration to compute intermediate correlations. Simulation results demonstrate that the …

Errata And Comments For Methods Of Statistical Model Estimation, Joseph M. Hilbe, Andew P. Robinson

Joseph M Hilbe

Errata and comments for Hilbe and Robinson's Methods of Statistical Model Estimation, Chapman & Hall/CRC (2013)

Optimizing Sedative Dose In Preterm Infants Undergoing Treatment For Respiratory Distress Syndrome, Peter F. Thall

Peter F. Thall

No abstract provided.

Bayesian Joint Selection Of Genes And Pathways: Applications In Multiple Myeloma Genomics, Lin Zhang, Jeffrey S. Morris, Jiexin Zhang, Robert Orlowski, Veerabhadran Baladandayuthapani

Jeffrey S. Morris

It is well-established that the development of a disease, especially cancer, is a complex process that results from the joint effects of multiple genes involved in various molecular signaling pathways. In this article, we propose methods to discover genes and molecular pathways significantly associ- ated with clinical outcomes in cancer samples. We exploit the natural hierarchal structure of genes related to a given pathway as a group of interacting genes to conduct selection of both pathways and genes. We posit the problem in a hierarchical structured variable selection (HSVS) framework to analyze the corresponding gene expression data. HSVS methods conduct …

Sas Macro: Testing Marginal Homogeneity In Clustered Matched-Pair Data, Zhao Yang

Zhao (Tony) Yang, Ph.D.

The SAS Macro and simulated data example are used to demonstrate the application of tests for marginal homogeneity in clustered matched-pair data.

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

Zhao (Tony) Yang, Ph.D.

This SAS macro calculate the weighted kappa statistic and its corresponding non-parametric variance estimator for the clustered matched-pair ordinal data.

Sas Macro: Kappa Statistic For Clustered Physician-Patients Polytomous Data, Zhao Yang

Zhao (Tony) Yang, Ph.D.

This SAS macro calculate the kappa statistic and its semi-parametric variance estimator for the clustered physician-patients polytomous data. The proposed method depends on the assumption of conditional independence for the clustered physician-patients data structure.

Compound Interest And The Power Of Saving, Richard H. Serlin

Richard H. Serlin

This is an article with an included assignment that I give to my personal finance 1 students. The first part talks about the power of compound interest. I go into depth about the intuition why it's so powerful, why it takes off, and has been called the eighth wonder of the world. I've haven't seen anywhere else an extensive explanation of the intuition like I have here.

In the second part I give the students a nice assignment to see how much their savings can grow if they invest even a modest amount consistently, month in and month out, in …

Combining Biomarkers Linearly And Nonlinearly For Classification Using The Area Under The Roc Curve, Youyi Fong, Shuxin Yin, Ying Huang

Youyi Fong

In biomedical studies, it is often of interest to classify/predict a subject's disease status based on some biomarker measurements. Two approaches have received a lot of attention in the biostatistical literature for finding optimal biomarker combinations using a training data. The likelihood approach maximizes logistic regression model likelihood, while the AUC (area under the receiver operating characteristic curve) approach maximizes the empirical AUC based on biomarker combination. The two approaches are complementary to each other in practice. Existing methods in the AUC approach either approximate the empirical AUC by a smooth function or replace it with a convex upper bound. …

Causal Models And Learning From Data: Integrating Causal Modeling And Statistical Estimation, Maya Petersen, M J. Van Der Laan

Maya Petersen

No abstract provided.

Targeted Maximum Likelihood Estimation For Dynamic And Static Longitudinal Marginal Structural Working Models, Maya Petersen, J Schwab, S Gruber, N Blaser, M Schomaker, M J. Van Der Laan

Maya Petersen

No abstract provided.

An Asymptotically Minimax Kernel Machine, Debashis Ghosh

Debashis Ghosh

Recently, a class of machine learning-inspired procedures, termed kernel machine methods, has been extensively developed in the statistical literature. It has been shown to have large power for a wide class of problems and applications in genomics and brain imaging. Many authors have exploited an equivalence between kernel machines and mixed eects models and used attendant estimation and inferential procedures. In this note, we construct a so-called `adaptively minimax' kernel machine. Such a construction highlights the role of thresholding in the observation space and limits on the interpretability of such kernel machines.

Multiple Comparison Procedures For Neuroimaging Genomewide Association Studies, Wen-Yu Hua, Thomas E. Nichols, Debashis Ghosh

Debashis Ghosh

Recent research in neuroimaging has been focusing on assessing associations between genetic variants measured on a genomewide scale and brain imaging phenotypes. Many publications in the area use massively univariate analyses on a genomewide basis for finding single nucleotide polymorphisms that influence brain structure. In this work, we propose using various dimensionalityreduction methods on both brain MRI scans and genomic data, motivated by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustments inspired from the idea of local false discovery rate of Efron and others (2001). Our proposed procedure is able to find associations …

On Likelihood Ratio Tests When Nuisance Parameters Are Present Only Under The Alternative, Cz Di, K-Y Liang

Chongzhi Di

In parametric models, when one or more parameters disappear under the null hypothesis, the likelihood ratio test statistic does not converge to chi-square distributions. Rather, its limiting distribution is shown to be equivalent to that of the supremum of a squared Gaussian process. However, the limiting distribution is analytically intractable for most of examples, and approximation or simulation based methods must be used to calculate the p values. In this article, we investigate conditions under which the asymptotic distributions have analytically tractable forms, based on the principal component decomposition of Gaussian processes. When these conditions are not satisfied, the principal …

Hypothesis Testing For An Extended Cox Model With Time-Varying Coefficients, Takumi Saegusa, Chongzhi Di, Ying Qing Chen

Chongzhi Di

In many randomized clinical trials, the log-rank test has routinely been used to detect a treatment effect under the Cox proportional hazards model for censored time-to-event outcomes. However, it may lose power substantially when the proportional hazards assumption does not hold. There are approaches to testing the proportionality, such as the smoothing spline-based score test by Lin, Zhang and Davidian (2006). In this paper, we consider an extended Cox model assuming time-varying treatment effect. We then use smoothing splines to model the time-varying treatment effect, and we propose spline-based score tests for the overall treatment effect. Our proposed tests take …