2. 您的位置
3. 首页
4. 会议论文
5. Readmission Time Analysis for Psychiatry Patients Based on a Chi-Squared Goodness of Fit Test for Re

Readmission Time Analysis for Psychiatry Patients Based on a Chi-Squared Goodness of Fit Test for Re

来源 :上海交通大学 | 被引量 : 0次 | 上传用户：ytvct

【摘 要】

：

Hospitalizations account for one-third of the annual healthcare costs in the USA.A large percentage of hospital readmissions can be avoided or prevented.

【作 者】

：

Qing Li Gideon Zamba 

【机 构】

：

Department of Biostatistics The University of Iowa,IA,52241,USA

【出 处】

：

上海交通大学

【发表日期】

：

2016年4期

【关键词】

：

Recurrent Events Goodness of fit Tests 

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

　　Hospitalizations account for one-third of the annual healthcare costs in the USA.A large percentage of hospital readmissions can be avoided or prevented.

其他文献

Active learning in binary classification problems via stochastic linear regression

In most classification applications,classifiers are trained with labeled data.Modern computing and communication technologies can make data collection procedures very efficient.

会议

ROC curveActive learningStopping ruleD-optimal designShrinkage estimate

Integrated analysis of multidimensional omics data

In the study of cancer and other complex diseases,multiple types of omics measurements(including mRNA gene expression,methylation,copy number variation,SNP,etc.)have been found to play important roles

会议

Integrated analysisdouble Laplacian shrinkagenetwork adjacency

A Self-Starting Chart to Monitor Abrupt Changes in General Linear Profiles

A self-starting monitoring scheme is proposed in this paper for simultaneous detection of variance and coefficients in linear profiles with unknown error distribution.

会议

SPCWald-type statisticlinear profileself-starting

Matrix-free REML for Linear Spatial-temporal State-space Models using H-likelihood Methods

We combines Besags spatial-temporal Gaussian autoregressions within a state-space model and presents a novel matrix-free h-likelihood method for statistical inference.

会议

H-likelihoodKalman filterLanczos algorithmMatrix-free computationState space

ROBUST RIDGE REGRESSION ESTIMATORS FOR NONLINEAR MODELS WITH APPLICATIONS TO HIGH THROUGHPUT SCREENI

Nonlinear regression is often used to evaluate the toxicity of a chemical or a drug by fitting data from a dose-response study.

会议

doseresponseHTS assayM-estimationpharmacologyridge regressiontoxicology

A FALSE DISCOVERY APPROACH FOR SCANNING SPATIAL DISEASE CLUSTERS WITH ARBITRARY SHAPES

The spatial scan statistic is one of the main tools for testing the presence of clusters in a geographical region.

会议

Poisson distributionscan statisticsfalse discovery ratesimple linear step-up

Integrating genotype and phenotype data to uncover therapeutic targets and markers in cancer

We integrated microarray gene expression data(GED),668 published synthetic lethal(SL)gene pairs,protein expression data(immunohistochemistry(IHC))and patients clinical factors to uncover novel treatme

会议

lung adenocarcinomaTP53prognosis markersynthetic lethalgene expression data

Seasonal Precipitation Prediction via Data-Adaptive Principal Component Regression

This talk studies a problem of predicting seasonal precipitation over East Asia from real observations and multi-model ensembles.

会议

Data-adaptive principal component analysisHigh-dimensional dataPrecipitationP

Trimmed estimators for large dimensional sparse covariance matrices

In this paper we will propose two new estimators for sparse covariance matrix.Our starting point is to make the estimator of each element of covariance matrix more robust.

会议

covariance matrixsparsethresholdingtrimmed estimator

Quantile regression for partially linear varying-coefficient model with censoring indicators missing

In this paper,we focus on the partially linear varying-coefficient quantile regression model when the data are right censored and the censoring indicators of right censored data are missing at random.

会议

Quantile regressionpartially linear varying-coefficient modelvariable selectio