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- Amplifications (1)
- Asthma; Cluster Detection; Cumulative Residuals; Martingales; Spatial Scan Statistic (1)
- BLUPs; Kernel function; Model/variable selection; Nonparametric regression; Penalized likelihood; REML; Score test; Smoothing parameter; Support vector machines (1)
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Articles 1 - 7 of 7
Full-Text Articles in Statistics and Probability
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Harvard University Biostatistics Working Paper Series
An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for …
Bayesian Hidden Markov Modeling Of Array Cgh Data, Subharup Guha, Yi Li, Donna Neuberg
Bayesian Hidden Markov Modeling Of Array Cgh Data, Subharup Guha, Yi Li, Donna Neuberg
Harvard University Biostatistics Working Paper Series
Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for …
Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li
Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Evaluating Prediction Rules For T-Year Survivors With Censored Regression Models, Hajime Uno, Tianxi Cai, Lu Tian, L.J. Wei
Evaluating Prediction Rules For T-Year Survivors With Censored Regression Models, Hajime Uno, Tianxi Cai, Lu Tian, L.J. Wei
Harvard University Biostatistics Working Paper Series
Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, …
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Harvard University Biostatistics Working Paper Series
No abstract provided.