screening of variables

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Variable Screening in High-dimensional Feature Space - Operations ...

Variable selection in high-dimensional space characterizes many contemporary prob- lems in scientific discovery and decision making. Fan and Lv [8] introduced the concept of sure screening to reduce the dimensionality. This article first reviews the part of their ideas and results and then extends them to the likelihood...

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Variable Screening - NCSU Statistics

You will often have many candidate variables to use as independent variables in a regression model. Using all of them may be infeasible (more parameters than observations). Even if feasible, a prediction equation with many parameters may not perform well: in validation; in application. 1 / 22. Variable Screening Methods.

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GitHub - wwrechard/screening: An R package for efficient variable ...

README.md. screening. An R package for efficient variable screening for linear and generalized linear models. Description. This package implements four popular variable screening algorithms, the sure independence screening (SIS), high-dimensional ordinary least squares projection (HOLP), rank-robust correlation...

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HOLP for Screening Variables - UCL

Screen first, refine next. Intuition: choosing a superset ˆM⊇MS is much easier than estimating the exact set ˆM = MS. • Actually widely used before any theory was available. • Fan and Lv (2008): a theory for screening by retaining variables with large marginal correlations: Sure. Independent Screening (SIS). • Marginal:...

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High-dimensional Ordinary Least-squares Projection for Screening ...

Jun 5, 2015 ... To overcome this, we propose a novel and simple screening technique called the high-dimensional ordinary least-squares projection (HOLP). We show that HOLP possesses the sure screening property and gives consistent variable selection without the strong correlation assumption, and has a low...

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Variable screening method using statistical sensitivity analysis in ...

VARIABLE SCREENING METHOD. USING STATISTICAL SENSITIVITY ANALYSIS IN RBDO by. Sangjune Bae. A thesis submitted in partial fulfillment of the requirements for the Master of Science degree in Mechanical Engineering in the Graduate College of. The University Of Iowa. May 2012. Thesis Supervisor: Professor...

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High dimensional ordinary least squares projection for screening ...

Nov 8, 2015 ... Summary. Variable selection is a challenging issue in statistical applications when the number of predictors p far exceeds the number of observations n. In this ultrahigh dimensional setting, the sure independence screening procedure was introduced to reduce the dimensionality significantly by preserving...

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Score test variable screening - NCBI - NIH

Aug 14, 2014 ... Variable screening has emerged as a crucial first step in the analysis of high-throughput data, but existing procedures can be computationally cumbersome, difficult to justify theoretically, or inapplicable to certain types of analyses. Motivated by a high-dimensional censored quantile regression problem in...

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Practical Issues in Screening and Variable Selection in Genome ...

Jan 14, 2015 ... Numerous methods have been proposed to handle GWAS data. Most statistical methods have adopted a two-stage approach: pre-screening for dimensional reduction and variable selection to identify causal SNPs. The pre-screening step selects SNPs in terms of their P-values or the absolute values of the...

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High-dimensional Ordinary Least-squares Projection for Screening ...

Screening Variables. Xiangyu Wang and Chenlei Leng. ∗. Abstract. Variable selection is a challenging issue in statistical applications when the number of predictors p far exceeds the number of observations n. In this ultra-high dimensional setting, the sure independence screening (SIS) procedure was introduced to...

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Screening Variables for Multilateral Technology - OnePetro

This article is a synopsis of paper SPE 64698, "Screening Variables for Multilateral Technology," by Ray Brister, SPE, Chevron Petroleum Technology Co., originally presented at the 2000 SPE International Oil and Gas Conference and Exhibition in Chin.

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Identifying key variables in African American adherence to colorectal ...

Identifying key variables in African American adherence to colorectal cancer screening: the application of data mining. Vetta L Sanders ThompsonEmail author,; Sean Lander,; Shuyu Xu and; Chi-Ren Shyu. BMC Public Health201414:1173. https://doi.org/10.1186/1471-2458-14-1173. © Thompson et al.; licensee BioMed...

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081-2007: Variable Screening for Multinomial Logistic Regression ...

However, using multinomial logistic regression presents some challenges. We are often faced with very large sample sizes and a large number of candidate predictor variables. The sheer sizes of our data sets make variable selection and scoring significant issues. The functionality that SAS has built into PROC LOGISTIC to...

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Design and Analysis of Computer Experiments for Screening Input ...

for Screening Input Variables. Dissertation. Presented in Partial Fulfillment of the Requirements for the Degree Doctor of. Philosophy in the Graduate School of The Ohio State University. By. Hyejung Moon, M.S.. Graduate Program in Statistics. The Ohio State University. 2010. Dissertation Committee: Thomas J. Santner, Co-...

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"Model-Free Variable Screening, Sparse Regression Analysis and ...

Variable screening and variable selection methods play important roles in modeling high dimensional data. Variable screening is the process of filtering out irrelevant variables, with the aim to reduce the dimensionality from ultrahigh to high while retaining all important variables. Variable selection is the process of selecting...

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Forward Regression for Ultra-High Dimensional Variable Screening

Electronic copy available at: http://ssrn.com/abstract=1376127. Forward Regression for Ultra-High Dimensional. Variable Screening. Hansheng Wang. Guanghua School of Management, Peking University. This version: April 9, 2009. Abstract. Motivated by the seminal theory of Sure Independence Screening (Fan and Lv,.

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Getting To Know Stock Screeners - Investopedia

By focusing on the measurable factors affecting a stock's price, stock screeners help their users perform quantitative analysis. In other words, screening focuses on tangible variables such as market capitalization, revenue, volatility and profit margins, as well as performance ratios such as the P/E ratio or debt-to-equity ratio.

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Automotive crashworthiness design using response surface‐based ...

The method illustrated provides a practical approach to the screening of variables in simulation‐based design optimization, especially in automotive crashworthiness applications with costly simulations. It is shown that the reduction of variables used in the optimization process significantly reduces the total cost of the...

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Marginal Screening for Partial Least Squares Regression - IEEE ...

Jul 18, 2017 ... Recently, PLS-based variable selection has attracted great attention due to high-throughput data reduction and modeling interpretability. In this paper, a class of variable selection methods for PLS, which employs marginal screening approaches to select relevant variables, is proposed. The proposed...

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Data screening - Wikiversity

Mar 19, 2017 ... In SPSS, in the Data View, cases can be sorted by variables with out-of-range values in order to easily identify the case(s) which has(have) the out-of-range values near the top and/or bottom of the data file. Alternatively, a search and find could be used to identify the cell(s) which contain(s) the out-of-range...

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Predictor Variables and Screening Protocol for Depressive and ...

Mar 8, 2016 ... Therefore, the objectives of this study are to identify predictor variables (demographic and clinical) for the development of mood and anxiety disorders in cancer outpatients and to propose a probabilistic screening protocol considering these variables and certain standardized screening instruments.

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Variable Screening via Quantile Partial Correlation: Journal of the ...

In quantile linear regression with ultrahigh-dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best...

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Screening of Most Effective Variables for Development of ... - Hindawi

Jul 6, 2013 ... Further, the screening techniques employed as a part of DoE help in finding the “important” and “unimportant” input variables. One can simulate the product or process behavior using model equation(s) and thus save a significant amount of resources, namely, time, effort, materials, and cost. The remarkable...

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Sensitivity Analysis and Variable Screening - ISyE

However, the two notions are related and variable screening proce- dures use some form of SA to assess the activity of each candidate input. Hence SA will be described first and then, using SA tools, two approaches to variable selection will be presented. To fix ideas concerning SA, consider the function y(x1, x2) = x1 + x2.

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Quality by design: screening of critical variables and formulation ...

The study was aimed at screening, understanding, and optimizing product variability of dutasteride-loaded Eudragit E nanoparticles prepared by solvent displacement using Plackett–Burman screening and a central composite design. The independent process and formulation factors selected included: drug loading (%)...

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SIS: An R Package for Sure Independence Screening in Ultrahigh ...

Abstract. We revisit sure independence screening procedures for variable selection in generalized linear models and the Cox proportional hazards model. Through the publicly available. R package SIS, we provide a unified environment to carry out variable selection using iterative sure independence screening (ISIS) and...

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Screening and Evaluation of Variables for Determination of ... - MDPI

Aug 5, 2016 ... Screening and Evaluation of Variables for. Determination of Sulfonylurea Herbicides in. Water Samples by Capillary Zone Electrophoresis. Valeria Springer *, Carolina V. Di Anibal and Adriana G. Lista. INQUISUR (CONICET-UNS), Departamento de Química, Universidad Nacional del Sur, Alem Av. 1253,.

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Screening System

There are many variables which affect the amount and type of smoke that is produced and how it will be dispersed. Also, limited research has been done to determine how these variables affect smoke. This system utilizes only the major parameters and is based on an worse-average weather and fuel conditions and...

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Partition-based ultrahigh-dimensional variable screening ...

Oct 9, 2017 ... Summary. Traditional variable selection methods are compromised by overlooking useful information on covariates with similar functionality or spatial proximity.

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The Relationship Between Select Variables and the Breast Cancer ...

THE RELATIONSHIP BETWEEN SELECT VARIABLES AND THE BREAST CANCER SCREENING PRACTICES OF A CONVIENENT SAMPLE OF AFRICAN-AMERICAN WOMEN FROM GRAMBLING STATE UNIVERSITY AND THE WILLIS-KNIGHTON NEIGHBORHOOD CLINIC ABSTRACT By: Karma M. Rabon-Stith...

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Efficiently Screening Predictor Variables for Logistic ... - Lex Jansen

Efficiently Screening Predictor Variables for Logistic Models. Steven Raimi, Marketing Associates, Detroit, MI and Wilmington, DE. Bruce Lund, Marketing Associates, Detroit, MI and Wilmington, DE. ABSTRACT. This paper discusses the situation where a modeler must fit a multinomial logistic model with nominal target and...

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Data Screening Check List - CSUN

Among continuous variables – whether searching for univariate or multivariate outliers the method depends on whether the data is grouped or not. If you are performing analyses with ungrouped data (i.e. regression, canonical correlation, factor analysis, or structural equations modeling) univariate and multivariate outliers...

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Two Tales of Variable Selection for High ... - Semantic Scholar

Variable selection plays an important role in high dimensional regression problems where a large number of variables are given as potential predictors of a response of interest. Typically, it arises at two stages of statistical modeling, namely screening and formal model building, with different goals. Screening aims at filtering...

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Newborn Thyroid Screening: Influence of Pre-Analytic Variables on ...

Aug 16, 2017 ... Newborn Thyroid Screening: Influence of Pre-Analytic Variables on Dried Blood Spot Thyrotropin Measurement. To cite this article: Butler Allison M., Charoensiriwatana Wiyada, Krasao Piamnukul, Pankanjanato Rotjanapan, Thong-Ngao Penpan, Polson Randall C., Snow Gregory, and Ehrenkranz Joel.

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"Identification of Variables that led to improvement in Breast Cancer ...

Our goal was to increase the compliance rates of screening mammogram by at least 10% over a 10 month period by utilizing American Cancer Society grant. Methods: Baseline data was obtained by retrospective chart review of women between ages 50 to 74 who visited CMC from January 2012 to December 2012 (Group...

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WoE and IV Variable Screening with {Information} in R | R-bloggers

Aug 27, 2017 ... A short note on information-theoretic variable screening in R w. {information}. Variable screening comes as an important step in the contemporary EDA for.

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Comparison of different screening methods - Bentley University

Multiple group screening. •. Sequential bifurcation. Before presenting the different methods, we provide more details about the case study. 1. Illustrative example. 1.1. Formalization. A phenomenon can be considered as a black box, characterized by: • variables of environment,uenv, generally badly known and numerous,.

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Optimality of Graphlet Screening in High Dimensional Variable ...

Submitted 5/12; Revised 1/14; Published 8/14. Optimality of Graphlet Screening in High Dimensional. Variable Selection. Jiashun Jin jiashun@stat.cmu.edu. Department of Statistics. Carnegie Mellon University. Pittsburgh, PA 15213, USA. Cun-Hui Zhang cunhui@stat.rutgers.edu. Department of Statistics. Rutgers University.

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High-dimensional variable screening and bias in subsequent ...

Sep 3, 2012 ... Abstract. We review variable selection and variable screening in high-dimen- sional linear models. Thereby, a major focus is an empirical comparison of various estimation methods with respect to true and false positive selection rates based on 128 different sparse scenarios from semi-real data (real data...

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Score test variable screening - Collection of Biostatistics Research ...

Abstract. Variable screening has emerged as a crucial first step in the analysis of high-throughput data, but existing procedures can be computationally cumbersome, difficult to justify theoretically, or inapplicable to certain types of analyses. Motivated by a high-dimensional censored quantile regression problem in multiple...

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