The discrete element method(DEM) has been widely used to simulate microscopic interactions between particles.Screening is a deeply complicated process when considering the law of motion for the particles,themselves.In...The discrete element method(DEM) has been widely used to simulate microscopic interactions between particles.Screening is a deeply complicated process when considering the law of motion for the particles,themselves.In this paper,a numerical model for the study of a particle screening process using the DEM is presented.Special attention was paid to the modeling of a vibrating screen that allows particles to pass through,or to rebound,when approaching the screen surface.Inferences concerning screen length and vibrating frequency as they relate to screening efficiency were studied.The conclusions were:three-dimensional simulation of screening efficiency along the screen length follows an exponential distribution;when the sieve vibrates over a certain frequency range the screening efficiency is stable;and,higher vibration frequencies can improve the handling capacity of the screening machine.展开更多
High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional dat...High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.展开更多
Colorectal cancer(CRC)is the second most frequent malignant disease in Europe.Every year,412 000people are diagnosed with this condition,and 207 000patients die of it.In 2003,recommendations forscreening programs were...Colorectal cancer(CRC)is the second most frequent malignant disease in Europe.Every year,412 000people are diagnosed with this condition,and 207 000patients die of it.In 2003,recommendations forscreening programs were issued by the Council of the European Union(EU),and these currently serve as thebasis for the preparation of European guidelines forCRC screening.The manner in which CRC screening iscarried out varies significantly from country to countrywithin the EU,both in terms of organization and thescreening test chosen.A screening program of onesort or another has been implemented in 19 of 27 EUcountries.The most frequently applied method is testing stool for occult bleeding(fecal occult blood test,FOBT).In recent years,a screening colonoscopy hasbeen introduced,either as the only method(Poland)orthe method of choice(Germany,Czech Republic).展开更多
Feature screening plays an important role in ultrahigh dimensional data analysis.This paper is concerned with conditional feature screening when one is interested in detecting the association between the response and ...Feature screening plays an important role in ultrahigh dimensional data analysis.This paper is concerned with conditional feature screening when one is interested in detecting the association between the response and ultrahigh dimensional predictors(e.g.,genetic makers)given a low-dimensional exposure variable(such as clinical variables or environmental variables).To this end,we first propose a new index to measure conditional independence,and further develop a conditional screening procedure based on the newly proposed index.We systematically study the theoretical property of the proposed procedure and establish the sure screening and ranking consistency properties under some very mild conditions.The newly proposed screening procedure enjoys some appealing properties.(a)It is model-free in that its implementation does not require a specification on the model structure;(b)it is robust to heavy-tailed distributions or outliers in both directions of response and predictors;and(c)it can deal with both feature screening and the conditional screening in a unified way.We study the finite sample performance of the proposed procedure by Monte Carlo simulations and further illustrate the proposed method through two real data examples.展开更多
基金Project 2006HZ0002-2 supported by the Special Topic Fund of Key Science and Technology of Fujian Province
文摘The discrete element method(DEM) has been widely used to simulate microscopic interactions between particles.Screening is a deeply complicated process when considering the law of motion for the particles,themselves.In this paper,a numerical model for the study of a particle screening process using the DEM is presented.Special attention was paid to the modeling of a vibrating screen that allows particles to pass through,or to rebound,when approaching the screen surface.Inferences concerning screen length and vibrating frequency as they relate to screening efficiency were studied.The conclusions were:three-dimensional simulation of screening efficiency along the screen length follows an exponential distribution;when the sieve vibrates over a certain frequency range the screening efficiency is stable;and,higher vibration frequencies can improve the handling capacity of the screening machine.
基金supported by National Natural Science Foundation of China(Grant Nos.11401497 and 11301435)the Fundamental Research Funds for the Central Universities(Grant No.T2013221043)+3 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry,the Fundamental Research Funds for the Central Universities(Grant No.20720140034)National Institute on Drug Abuse,National Institutes of Health(Grant Nos.P50 DA036107 and P50 DA039838)National Science Foundation(Grant No.DMS1512422)The content is solely the responsibility of the authors and does not necessarily represent the official views of National Institute on Drug Abuse, National Institutes of Health, National Science Foundation or National Natural Science Foundation of China
文摘High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.
基金Supported by International Agency for Research on Cancer(Lawrence von Karsa,MD)International Digestive Cancer Alliance(Professor Meinhard Classen,MD,Professor Sidney JWinawer,MD)
文摘Colorectal cancer(CRC)is the second most frequent malignant disease in Europe.Every year,412 000people are diagnosed with this condition,and 207 000patients die of it.In 2003,recommendations forscreening programs were issued by the Council of the European Union(EU),and these currently serve as thebasis for the preparation of European guidelines forCRC screening.The manner in which CRC screening iscarried out varies significantly from country to countrywithin the EU,both in terms of organization and thescreening test chosen.A screening program of onesort or another has been implemented in 19 of 27 EUcountries.The most frequently applied method is testing stool for occult bleeding(fecal occult blood test,FOBT).In recent years,a screening colonoscopy hasbeen introduced,either as the only method(Poland)orthe method of choice(Germany,Czech Republic).
基金supported by National Science Foundation of USA (Grant No. P50 DA039838)the Program of China Scholarships Council (Grant No. 201506040130)+6 种基金 National Natural Science Foundation of China (Grant No. 11401497)the Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry, the National Key Basic Research Development Program of China (Grant No. 2010CB950703)the Fundamental Research Funds for the Central UniversitiesNational Institute on Drug AbuseNational Institutes of Health (Grants Nos. P50 DA036107 and P50 DA039838)National Science Foundation of USA (Grant No. DMS 1512422)
文摘Feature screening plays an important role in ultrahigh dimensional data analysis.This paper is concerned with conditional feature screening when one is interested in detecting the association between the response and ultrahigh dimensional predictors(e.g.,genetic makers)given a low-dimensional exposure variable(such as clinical variables or environmental variables).To this end,we first propose a new index to measure conditional independence,and further develop a conditional screening procedure based on the newly proposed index.We systematically study the theoretical property of the proposed procedure and establish the sure screening and ranking consistency properties under some very mild conditions.The newly proposed screening procedure enjoys some appealing properties.(a)It is model-free in that its implementation does not require a specification on the model structure;(b)it is robust to heavy-tailed distributions or outliers in both directions of response and predictors;and(c)it can deal with both feature screening and the conditional screening in a unified way.We study the finite sample performance of the proposed procedure by Monte Carlo simulations and further illustrate the proposed method through two real data examples.
