Physical Sciences and Mathematics Commons^™

Hilbe-Pglr-Errata-And-Comments, Joseph M. Hilbe

Joseph M Hilbe

Errata and Comments for Practical Guide to Logistic Regression

Functional Car Models For Spatially Correlated Functional Datasets, Lin Zhang, Veerabhadran Baladandayuthapani, Hongxiao Zhu, Keith A. Baggerly, Tadeusz Majewski, Bogdan Czerniak, Jeffrey S. Morris

Jeffrey S. Morris

We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on …

Embアルゴリズムの新たな応用による多重比率補定(高橋将宜), Masayoshi Takahashi

Masayoshi Takahashi

No abstract provided.

Pglr-Sas Data, Joseph M. Hilbe

Joseph M Hilbe

SAS data files for Practical Guide to Logistic Regression

R Code For Practical Guide To Logistic Regression, Joseph M. Hilbe

Joseph M Hilbe

R code for Practical Guide to Logistic Regression

Pglr-Stata Data Files, Joseph M. Hilbe

Joseph M Hilbe

Stata data files for Practical Guide to Logistic Regression

Sas Code Only For Practical Guide To Logistic Regression, Joseph M. Hilbe

Joseph M Hilbe

SAS code-only for Practical Guide to Logistic Regression

Sas Code & Output For Practical Guide To Logistic Regression, Joseph M. Hilbe

Joseph M Hilbe

SAS code for Practical Guide to Logistic Regression

Negative Binomial Regerssion, 2nd Ed, 2nd Print, Errata And Comments, Joseph Hilbe

Joseph M Hilbe

Errata and Comments for 2nd printing of NBR2, 2nd edition. Previous errata from first printing all corrected. Some added and new text as well.

Bayesian Function-On-Function Regression For Multi-Level Functional Data, Mark J. Meyer, Brent A. Coull, Francesco Versace, Paul Cinciripini, Jeffrey S. Morris

Jeffrey S. Morris

Medical and public health research increasingly involves the collection of more and more complex and high dimensional data. In particular, functional data|where the unit of observation is a curve or set of curves that are finely sampled over a grid -- is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data, presenting a simple model as well as a more extensive mixed model framework, along with multiple functional posterior …

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 and …

Ordinal Probit Wavelet-Based Functional Models For Eqtl Analysis, Mark J. Meyer, Jeffrey S. Morris, Craig P. Hersh, Jarret D. Morrow, Christoph Lange, Brent A. Coull

Jeffrey S. Morris

Current methods for conducting expression Quantitative Trait Loci (eQTL) analysis are limited in scope to a pairwise association testing between a single nucleotide polymorphism (SNPs) and expression probe set in a region around a gene of interest, thus ignoring the inherent between-SNP correlation. To determine association, p-values are then typically adjusted using Plug-in False Discovery Rate. As many SNPs are interrogated in the region and multiple probe-sets taken, the current approach requires the fitting of a large number of models. We propose to remedy this by introducing a flexible function-on-scalar regression that models the genome as a functional outcome. The …

The Number Of Subjects Per Variable Required In Linear Regression Analyses, Peter Austin, Ewout Steyerberg

Peter Austin

Objectives: To determine the number of independent variables that can be included in a linear regression model.

Study Design and Setting: We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R2 of the fitted model.

Results: A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, …

Moving Towards Best Practice When Using Inverse Probability Of Treatment Weighting (Iptw) Using The Propensity Score To Estimate Causal Treatment Effects In Observational Studies, Peter Austin, Elizabeth Stuart

Peter Austin

The propensity score is defined as a subject’s probability of treatment selection, conditional on observed baseline covariates.Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in …

Simulating Burr Type Vii Distributions Through The Method Of L-Moments And L-Correlations, Mohan D. Pant, Todd C. Headrick

Mohan Dev Pant

Burr Type VII, a one-parameter non-normal distribution, is among the less studied distributions, especially, in the contexts of statistical modeling and simulation studies. The main purpose of this study is to introduce a methodology for simulating univariate and multivariate Burr Type VII distributions through the method of L-moments and L-correlations. The methodology can be applied in statistical modeling of events in a variety of applied mathematical contexts and Monte Carlo simulation studies. Numerical examples are provided to demonstrate that L-moment-based Burr Type VII distributions are superior to their conventional moment-based analogs in terms of distribution fitting and estimation. Simulation results …

Asimmetria Del Rischio Sistematico Dei Titolo Immobiliari Americani: Nuove Evidenze Econometriche, Paola De Santis, Carlo Drago

Carlo Drago

In questo lavoro riscontriamo un aumento del rischio sistematico dei titoli del mercato immobiliare americano nell’anno 2007 seguito da un ritorno ai valori iniziali nell’anno 2009 e si evidenzia la possibile presenza di break strutturali. Per valutare il suddetto rischio sistematico è stato scelto il modello a tre fattori di Fama e French ed è stata studiata la relazione tra l’extra rendimento dell’indice REIT, utilizzato come proxy dell’andamento dei titoli immobiliari americani, e l’extra rendimento dell’indice S&P500 rappresentativo del rendimento del portafoglio di mercato. I risultati confermano la presenza di un “Asymmetric REIT Beta Puzzle” coerentemente con alcuni precedenti studi …

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 …

Errata - Logistic Regression Models, Joseph Hilbe

Joseph M Hilbe

Errata for Logistic Regression Models, 4th Printing

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.

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: 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.

A Comparison Of 12 Algorithms For Matching On The Propensity Score, Peter C. Austin

Peter Austin

Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity …

The Use Of Propensity Score Methods With Survival Or Time-To-Event Outcomes: Reporting Measures Of Effect Similar To Those Used In Randomized Experiments, Peter C. Austin

Peter Austin

Propensity score methods are increasingly being used to estimate causal treatment effects in observational studies. In medical and epidemiological studies, outcomes are frequently time-to-event in nature. Propensity-score methods are often applied incorrectly when estimating the effect of treatment on time-to-event outcomes. This article describes how two different propensity score methods (matching and inverse probability of treatment weighting) can be used to estimate the measures of effect that are frequently reported in randomized controlled trials: (i) marginal survival curves, which describe survival in the population if all subjects were treated or if all subjects were untreated; and (ii) marginal hazard ratios. …

The Performance Of Different Propensity Score Methods For Estimating Absolute Effects Of Treatments On Survival Outcomes: A Simulation Study, Peter C. Austin

Peter Austin

Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when both relative and absolute measures of effect are reported. When outcomes are time-to-event in nature, the effect of treatment can also be quantified as the change in mean or median survival time due to treatment and the absolute reduction in the probability of the occurrence of an event within a specified duration of follow-up. We describe …

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.