A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error,
2011
Hebrew University
A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error, David M. Zucker, Malka Gorfine, Yi Li, Donna Spiegelman
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Proof Of Bell's Inequality In Quantum Mechanics Using Causal Interactions,
2011
Harvard School of Public Health
A Proof Of Bell's Inequality In Quantum Mechanics Using Causal Interactions, James M. Robins, Tyler J. Vanderweele, Richard D. Gill
COBRA Preprint Series
We give a simple proof of Bell's inequality in quantum mechanics which, in conjunction with experiments, demonstrates that the local hidden variables assumption is false. The proof sheds light on relationships between the notion of causal interaction and interference between particles.
Effectively Selecting A Target Population For A Future Comparative Study,
2011
Northwestern University
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Harvard University Biostatistics Working Paper Series
When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. …
Multiple Testing Of Local Maxima For Detection Of Peaks In Chip-Seq Data,
2011
Harvard School of Public Health and Dana Farber Cancer Institute
Multiple Testing Of Local Maxima For Detection Of Peaks In Chip-Seq Data, Armin Schwartzman, Andrew Jaffe, Yulia Gavrilov, Clifford A. Meyer
Harvard University Biostatistics Working Paper Series
No abstract provided.
Manova: Type I Error Rate Analysis,
2011
California Polytechnic State University, San Luis Obispo
Manova: Type I Error Rate Analysis, Christopher Dau Wei Ling
Statistics
No abstract provided.
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial,
2011
Stanford University School of Medicine
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Assessing Medicare Beneficiaries’ Strength‐Of‐Preference Scores For Health Care Options: How Engaging Does The Elicitation Technique Need To Be?,
2011
Dartmouth College
Assessing Medicare Beneficiaries’ Strength‐Of‐Preference Scores For Health Care Options: How Engaging Does The Elicitation Technique Need To Be?, Trafford Crump, Hilary A. Llewellyn-Thomas
Dartmouth Scholarship
The objective was to determine if participants’ strength‐of‐preference scores for elective health care interventions at the end‐of‐life (EOL) elicited using a non‐engaging technique are affected by their prior use of an engaging elicitation technique.
Variable Importance Analysis With The Multipim R Package,
2011
Division of Biostatistics, University of California, Berkeley
Variable Importance Analysis With The Multipim R Package, Stephan J. Ritter, Nicholas P. Jewell, Alan E. Hubbard
U.C. Berkeley Division of Biostatistics Working Paper Series
We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE) of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including …
A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing,
2011
Fox Chase Cancer Center
A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi
COBRA Preprint Series
Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The nonnegativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. In the last few years, NMF has been successfully applied in a variety of areas such as natural language processing, information retrieval, image processing, speech recognition …
Multiple Testing Of Local Maxima For Detection Of Unimodal Peaks In 1d,
2011
Harvard School of Public Health and Dana Farber Cancer Institute
Multiple Testing Of Local Maxima For Detection Of Unimodal Peaks In 1d, Armin Schwartzman, Yulia Gavrilov, Robert J. Adler
Harvard University Biostatistics Working Paper Series
No abstract provided.
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm,
2011
university of mumbai,India
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale
Johns Hopkins University, Dept. of Biostatistics Working Papers
Biomedical signals can arise from one or many sources including heart ,brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal are like electroencephalogram(EEG),electrocardiogram(ECG),etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The diagnosis of patient is based on visual observation of recorded ECG,EEG,etc, may not be accurate. To achieve better understanding , PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals . The immense scope in the field of biomedical-signal processing Independent Component Analysis( …
An Exploration Of Non-Detects In Environmental Data,
2011
California Polytechnic State University, San Luis Obispo
An Exploration Of Non-Detects In Environmental Data, Juliana Fajardo
Statistics
No abstract provided.
Management And Support Of Shared Integrated Library Systems,
2011
University of Nevada, Las Vegas
Management And Support Of Shared Integrated Library Systems, Jason Vaughan, Kristen Costello
Library Faculty Research
The University of Nevada, Las Vegas (UNLV) University Libraries has hosted and managed a shared integrated library system (ILS) since 1989. The system and the number of partner libraries sharing the system has grown significantly over the past two decades. Spurred by the level of involvement and support contributed by the host institution, the authors administered a comprehensive survey to current Innovative Interfaces libraries. Research findings are combined with a description of UNLV’s local practices to provide substantial insights into shared funding, support, and management activities associated with shared systems.
Propensity Score Analysis With Matching Weights,
2011
Cleveland Clinic
Propensity Score Analysis With Matching Weights, Liang Li
COBRA Preprint Series
The propensity score analysis is one of the most widely used methods for studying the causal treatment effect in observational studies. This paper studies treatment effect estimation with the method of matching weights. This method resembles propensity score matching but offers a number of new features including efficient estimation, rigorous variance calculation, simple asymptotics, statistical tests of balance, clearly identified target population with optimal sampling property, and no need for choosing matching algorithm and caliper size. In addition, we propose the mirror histogram as a useful tool for graphically displaying balance. The method also shares some features of the inverse …
Power Analysis For Alternative Tests For The Equality Of Means.,
2011
East Tennessee State University
Power Analysis For Alternative Tests For The Equality Of Means., Haiyin Li
Electronic Theses and Dissertations
The two sample t-test is the test usually taught in introductory statistics courses to test for the equality of means of two populations. However, the t-test is not the only test available to compare the means of two populations. The randomization test is being incorporated into some introductory courses. There is also the bootstrap test. It is also not uncommon to decide the equality of the means based on confidence intervals for the means of these two populations. Are all those methods equally powerful? Can the idea of non-overlapping t confidence intervals be extended to bootstrap confidence intervals? The powers …
A Comparison Of Spatial Prediction Techniques Using Both Hard And Soft Data,
2011
University of Nebraska-Lincoln
A Comparison Of Spatial Prediction Techniques Using Both Hard And Soft Data, Megan L. Liedtke Tesar
Department of Statistics: Dissertations, Theses, and Student Research
The overall goal of this research, which is common to most spatial studies, is to predict a value of interest at an unsampled location based on measured values at nearby sampled locations. To accomplish this goal, ordinary kriging can be used to obtain the best linear unbiased predictor. However, there is often a large amount of variability surrounding the measurements of environmental variables, and traditional prediction methods, such as ordinary kriging, do not account for an attribute with more than one level of uncertainty. This dissertation addresses this limitation by introducing a new methodology called weighted kriging. This prediction technique …
Analysis Of Morris Water Maze Data With Bayesian Statistical Methods,
2011
University of Nevada, Las Vegas
Analysis Of Morris Water Maze Data With Bayesian Statistical Methods, Maxym V. Myroshnychenko, Anton Westveld, Jefferson Kinney
Festival of Communities: UG Symposium (Posters)
Neuroscientists commonly use a Morris Water Maze to assess learning in rodents. In his kind of a maze, the subjects learn to swim toward a platform hidden in opaque water as they orient themselves according to the cues on the walls. This protocol presents a challenge to statistical analysis, because an artificial cut-off must be set for those experimental subjects that do not reach the platform so as they do not drown from exhaustion. This fact leads to the data being right censored. In our experimental data, which compares learning in rodents that have chemically induced symptoms of schizophrenia to …
A Broad Symmetry Criterion For Nonparametric Validity Of Parametrically-Based Tests In Randomized Trials,
2011
Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
A Broad Symmetry Criterion For Nonparametric Validity Of Parametrically-Based Tests In Randomized Trials, Russell T. Shinohara, Constantine E. Frangakis, Constantine G.. Lyketos
Johns Hopkins University, Dept. of Biostatistics Working Papers
Summary. Pilot phases of a randomized clinical trial often suggest that a parametric model may be an accurate description of the trial's longitudinal trajectories. However, parametric models are often not used for fear that they may invalidate tests of null hypotheses of equality between the experimental groups. Existing work has shown that when, for some types of data, certain parametric models are used, the validity for testing the null is preserved even if the parametric models are incorrect. Here, we provide a broader and easier to check characterization of parametric models that can be used to (a) preserve nonparametric validity …
Estimation And Testing In Targeted Group Sequential Covariate-Adjusted Randomized Clinical Trials,
2011
Laboratoire MAP5, Université Paris Descartes and CNRS
Estimation And Testing In Targeted Group Sequential Covariate-Adjusted Randomized Clinical Trials, Antoine Chambaz, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
This article is devoted to the construction and asymptotic study of adaptive group sequential covariate-adjusted randomized clinical trials analyzed through the prism of the semiparametric methodology of targeted maximum likelihood estimation (TMLE). We show how to build, as the data accrue group-sequentially, a sampling design which targets a user-supplied optimal design. We also show how to carry out a sound TMLE statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the i.i.d setting only so far), and how group-sequential testing applies on top of it. The procedure is robust (i.e., consistent even if the …
Targeted Maximum Likelihood Estimation For Dynamic Treatment Regimes In Sequential Randomized Controlled Trials,
2011
University of California, Berkeley, Division of Biostatistics
Targeted Maximum Likelihood Estimation For Dynamic Treatment Regimes In Sequential Randomized Controlled Trials, Paul Chaffee, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Sequential Randomized Controlled Trials (SRCTs) are rapidly becoming essential tools in the search for optimized treatment regimes in ongoing treatment settings. Analyzing data for multiple time-point treatments with a view toward optimal treatment regimes is of interest in many types of afflictions: HIV infection, Attention Deficit Hyperactivity Disorder in children, leukemia, prostate cancer, renal failure, and many others. Methods for analyzing data from SRCTs exist but they are either inefficient or suffer from the drawbacks of estimating equation methodology. We describe an estimation procedure, targeted maximum likelihood estimation (TMLE), which has been fully developed and implemented in point treatment settings, …
