2. 您的位置
3. 首页
4. 会议论文
5. POWER OF OPTIMAL SIGNAL DETECTION METHODS UNDER FINITE SAMPLE

POWER OF OPTIMAL SIGNAL DETECTION METHODS UNDER FINITE SAMPLE

来源 :上海交通大学 | 被引量 : 0次 | 上传用户：fjfhmtv

【摘 要】

：

In big data analysis for detecting rare and weak signals among n features,the Higher Criticism test(HC),Berk-Jones test(B-J),and some (o) -divergence tests have been proven optimal under the asymptoti

【作 者】

：

Hong Zhang Jiashun Jin Zheyang Wu 

【机 构】

：

Worcester Polytechnic Institute,01609,USA Carnegi

【出 处】

：

上海交通大学

【发表日期】

：

2016年4期

【关键词】

：

Higher criticism phi-divergence goodness-of-fit sparse mixture signal detection 

下载到本地 , 更方便阅读

下载此文 赞助VIP

声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架

论文部分内容阅读

　　In big data analysis for detecting rare and weak signals among n features,the Higher Criticism test(HC),Berk-Jones test(B-J),and some (o) -divergence tests have been proven optimal under the asymptotics of n→∞.

其他文献

Modelling asymmetric bivariate data using mixtures of rotated copulas

Copulas are widely used for modelling complex multivariate dependencies.Our study offers a framework for the application of pair-mixtures of rotated copulas to model asymmetric dependencies in bivaria

会议

Effect of Imputation of Missing Data in the State Inpatient Databases on Racial Disparities Research

Research Objective: Racial disparities(RD)in healthcare outcomes in the U.S.have been identified in recent decades for total joint arthroplasty,particularly total knee arthroplasty(TKA).

会议

Coupling Computer Models through Linking their Statistical Emulators

Gaussian processes,together with an objective Bayesian implementation of the processes,have become a common tool for emulating(approximating)complex computer models.

会议

Uncertainty QuantificationComputer modelsGaussian ProcessesBayesian statistic

Local Shrinkage Covariance Model

Estimation of covariance matrix function is fundamentally important for many problems.

会议

Local shrinkagecovariance matrix functionMEG neuroimaging

DECOrrelated feature space partitioning for distributed sparse regression

Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge.

会议

Distributed computingEmbarassingly parallelHigh dimensionalitySparse regressi

Prediction risk for global-local shrinkage regression

Predictive performance in shrinkage regression suffers from two major difficulties:(ⅰ)the amount of relative shrinkage is monotone in the singular values of the design matrix and(ⅱ)the amount of shrin

会议

Error Bounds for Sequential Monte Carlo Samplers for Multimodal Distributions

In this talk,we provide bounds on the asymptotic variance for a class of sequential Monte Carlo(SMC)samplers designed for approximating multimodal distributions.

会议

SMC samplersasymptotic variancesharp peaksCurie-Weiss modelPotts model

REDESIGNING STATISTICS EDUCATION FOR A DATA-RICH WORLD

The uses of statistics in daily life and in the workforce are changing rapidly as data become more available to everyone.

会议

Statistics educationstatistics curriculumstatistical reasoningstatistical lit

Nonparametric Inference of Right Censored Data Using Smoothing Splines

This paper introduces a penalized nonparametric maximum likelihood estimation of the log-hazard function in analyzing right censored data.

会议

Functional Bahadur representationNonparametric inferencePenalized likelihoodR

Bayesian time series regression analysis with nonparametric modeling of autocorrelation

Time series regression has two purposes-comprehend the functional dependence of variable of interest on covariates and forecast the dependent variable for future values of covariates.

会议

Autocorrelation functionBayesian frameworkNonparametric modelingWhittle likel