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- Variable selection (3)
- Measurement error (2)
- Oracle property (2)
- Surrogate endpoints (2)
- Survival analysis (2)
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- B splines (1)
- Bayes inference. (1)
- Bayesian estimation (1)
- Bias correction (1)
- Calibrated Bayes (1)
- Causal inference (1)
- Copula (1)
- Correlated data (1)
- Correlation (1)
- Dependent structure (1)
- Dysphagia (1)
- Error-prone covariate (1)
- Feature selection (1)
- Frailty (1)
- Generalized estimating equations (1)
- Graphical lasso (1)
- High-dimensional data (1)
- Incomplete data (1)
- Interactions (1)
- Latent variable model (1)
- Latent variables (1)
- Latent variables model (1)
- Likelihood theory (1)
- Linear discriminant analysis (1)
- Longitudinal analysis (1)
Articles 1 - 13 of 13
Full-Text Articles in Physical Sciences and Mathematics
Varying Index Coefficient Models, Shujie Ma, Peter Xuekun Song
Varying Index Coefficient Models, Shujie Ma, Peter Xuekun Song
The University of Michigan Department of Biostatistics Working Paper Series
It has been a long history of utilizing interactions in regression analysis to investigate interactive effects of covariates on response variables. In this paper we aim to address two kinds of new challenges resulted from the inclusion of such high-order effects in the regression model for complex data. The first kind arises from a situation where interaction effects of individual covariates are weak but those of combined covariates are strong, and the other kind pertains to the presence of nonlinear interactive effects. Generalizing the single index coefficient regression model (Xia and Li, 1999), we propose a new class of semiparametric …
Penalized Smoothed Partial Rank Estimator For The Nonparametric Transformation Survival Model With High-Dimensional Covariates, Wei Dai, Yi Li
Penalized Smoothed Partial Rank Estimator For The Nonparametric Transformation Survival Model With High-Dimensional Covariates, Wei Dai, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Microarray technology has the potential to lead to a better understanding of biological processes and diseases such as cancer. When failure time outcomes are also available, one might be interested in relating gene expression profiles to the survival outcome such as time to cancer recurrence or time to death. This is statistically challenging because the number of covariates greatly exceeds the number of observations. While the majority of work has focused on regularized Cox regression model and accelerated failure time model, they may be restrictive in practice. We relax the model assumption and and consider a nonparametric transformation model that …
Surrogacy Assessment Using Principal Stratification When Surrogate And Outcome Measures Are Multivariate Normal, Anna Conlon, Jeremy M.G. Taylor, Michael R. Elliott
Surrogacy Assessment Using Principal Stratification When Surrogate And Outcome Measures Are Multivariate Normal, Anna Conlon, Jeremy M.G. Taylor, Michael R. Elliott
The University of Michigan Department of Biostatistics Working Paper Series
No abstract provided.
Missing At Random And Ignorability For Inferences About Subsets Of Parameters With Missing Data, Roderick J. Little, Sahar Zanganeh
Missing At Random And Ignorability For Inferences About Subsets Of Parameters With Missing Data, Roderick J. Little, Sahar Zanganeh
The University of Michigan Department of Biostatistics Working Paper Series
For likelihood-based inferences from data with missing values, Rubin (1976) showed that the missing data mechanism can be ignored when (a) the missing data are missing at random (MAR), in the sense that missingness does not depend on the missing values after conditioning on the observed data, and (b) the parameters of the data model and the missing-data mechanism are distinct; that is, there are no a priori ties, via parameter space restrictions or prior distributions, between the parameters of the data model and the parameters of the model for the mechanism. Rubin described (a) and (b) as the "weakest …
In Praise Of Simplicity Not Mathematistry! Ten Simple Powerful Ideas For The Statistical Scientist, Roderick J. Little
In Praise Of Simplicity Not Mathematistry! Ten Simple Powerful Ideas For The Statistical Scientist, Roderick J. Little
The University of Michigan Department of Biostatistics Working Paper Series
Ronald Fisher was by all accounts a first-rate mathematician, but he saw himself as a scientist, not a mathematician, and he railed against what George Box called (in his Fisher lecture) "mathematistry". Mathematics is the indispensable foundation for statistics, but our subject is constantly under assault by people who want to turn statistics into a branch of mathematics, making the subject as impenetrable to non-mathematicians as possible. Valuing simplicity, I describe ten simple and powerful ideas that have influenced my thinking about statistics, in my areas of research interest: missing data, causal inference, survey sampling, and statistical modeling in general. …
Selection Of Latent Variables For Multiple Mixed-Outcome Models, Ling Zhou, Huazhen Lin, Xin-Yuan Song, Yi Li
Selection Of Latent Variables For Multiple Mixed-Outcome Models, Ling Zhou, Huazhen Lin, Xin-Yuan Song, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. As the formulation of a latent variable model is often unknown a priori, misspecification could distort the dependence structure and lead to unreliable model inference. More- over, the multiple outcomes are often of varying types (e.g., continuous and ordinal), which presents analytical challenges. In this article, we present a class of general latent variable models that can accommodate mixed types of outcomes, and further propose a novel selection approach that simultaneously selects latent variables and estimates model parameters. We show that the proposed estimators …
A Latent Variable Transformation Model Approach For Exploring Dysphagia, Anna Snavely, David P. Harrington, Yi Li
A Latent Variable Transformation Model Approach For Exploring Dysphagia, Anna Snavely, David P. Harrington, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
No abstract provided.
A Frailty Approach For Survival Analysis With Error-Prone Covariate, Sehee Kim, Yi Li, Donna Spiegelman
A Frailty Approach For Survival Analysis With Error-Prone Covariate, Sehee Kim, Yi Li, Donna Spiegelman
The University of Michigan Department of Biostatistics Working Paper Series
This paper discovers an inherent relationship between the survival model with covariate measurement error and the frailty model. The discovery motivates our using a frailty-based estimating equation to draw inference for the proportional hazards model with error-prone covariates. Our established framework accommodates general distributional structures for the error-prone covariates, not restricted to a linear additive measurement error model or Gaussian measurement error. When the conditional distribution of the frailty given the surrogate is unknown, it is estimated through a semiparametric copula function. The proposed copula-based approach enables us to fit flexible measurement error models without the curse of dimensionality as …
Ultrahigh Dimensional Time Course Feature Selection, Peirong Xu, Lixing Zhu, Yi Li
Ultrahigh Dimensional Time Course Feature Selection, Peirong Xu, Lixing Zhu, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Statistical challenges arise from modern biomedical studies that produce time course genomic data with ultrahigh dimensions. In a renal cancer study that motivated this paper, the pharmacokinetic measures of a tumor suppressor (CCI-779) and expression levels of 12625 genes were measured for each of 33 patients at 8 and 16 weeks after the start of treatments, with the goal of identifying predictive gene transcripts and the interactions with time in peripheral blood mononuclear cells for pharmacokinetics over the time course. The resulting dataset defies analysis even with regularized regression. Although some remedies have been proposed for both linear and generalized …
Covariance-Enhanced Discriminant Analysis, Peirong Xu, Ji Zhu, Lixing Zhu, Yi Li
Covariance-Enhanced Discriminant Analysis, Peirong Xu, Ji Zhu, Lixing Zhu, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Linear discriminant analysis (LDA), a classical method in pattern recognition and machine learning, has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high-dimensionality of the high-throughput features obtained from modern biological experiments, for example, microarray or proteomics, defies traditional discriminant analysis techniques. The possible interfeature correlations present additional challenges and are often under-utilized in modeling. In this paper, by incorporating the possible inter-feature correlations, we propose a Covariance-Enhanced Discriminant Analysis (CEDA) method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. We show that, under mild regularity conditions, …
Semiparametric Latent Variable Transformation Models For Multiple Mixed Outcomes, Huazhen Lin, Ling Zhou, Robert Elashoff, Yi Li
Semiparametric Latent Variable Transformation Models For Multiple Mixed Outcomes, Huazhen Lin, Ling Zhou, Robert Elashoff, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
No abstract provided.
Semiparametric Transformation Models For Semicompeting Survival Data, Huazhen Lin, Ling Zhou, Chunhong Li, Yi Li
Semiparametric Transformation Models For Semicompeting Survival Data, Huazhen Lin, Ling Zhou, Chunhong Li, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Semicompeting risk outcome data, e.g. time to disease progression and time to death, are commonly collected in clinical trials, but complicated analytical tools hamper the analysis and the interpretation of the results. We propose a novel semiparametric transformation model for such data. Compared with the existing models, our model is advantageous in the following distinctive ways. First, it allows us to provide direct estimators of the regression analysis and the association parameter. Second, the measure of surrogacy, for example, the proportion of treatment effect and relative effect, can also be directly obtained. We propose a two-stage estimation procedure for inference …
Score Test Variable Screening, Sihai Dave Zhao, Yi Li
Score Test Variable Screening, Sihai Dave Zhao, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
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 myeloma genomics, this paper makes three contributions. First, we establish a score test-based screening framework, which is widely applicable, extremely computationally efficient, and relatively simple to justify. Secondly, we propose a resampling-based procedure for selecting the number of variables to retain after screening according to the principle of reproducibility. Finally, we propose a new iterative score test …