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A model selection approach to genome wide association studies
Genome wide association Multiple testing Linear regression,
2010/10/14
For the vast majority of genome wide association studies (GWAS) published so far, statistical analysis was performed by testing markers individually. In this article we present some elementary statis...
A Generalized Publication Bias Model
publication bias meta-analysis file-drawer hypothesis fail-safe number
2010/4/30
Scargle (2000) has discussed Rosenthal&Rubin's (1978) "fail-safe number" (FSN)
method for estimating the number of unpublished studies in meta-analysis. He concluded
that this FSN cannot possibly be...
Finite Element Model Updating Using Bayesian Approach
Bayesian Maximum-likelihood finite element updating
2010/4/29
This paper compares the Maximum-likelihood method and Bayesian method for finite element model updating.The Maximum-likelihood method was implemented using genetic algorithm while the Bayesian method ...
A Bivariate Competing-Risks Model with One Termination Event
Competing-risks Survival analysis Bivariate Weibull
2010/4/26
The likelihood function for a competing-risks model with one fatal and one non-fatal
event is proposed. A bivariate Weibull using the likelihood function is applied to the
Stanford Heart Transplant ...
Ultrahigh dimensional variable selection for Cox's proportional hazards model
Ultrahigh dimensional variable selection Cox's proportional hazards model
2010/3/10
Variable selection in high dimensional space has challenged many
contemporary statistical problems from many frontiers of scientific disciplines.
Recent technology advance has made it possible to co...
Evolutionary Stochastic Search for Bayesian model exploration
Evolutionary Monte Carlo Fast Scan Metropolis-Hastings schemes Linear Gaussian regressionmodels Variable selection
2010/3/10
Implementing Bayesian variable selection for linear Gaussian regression models for analysing
high dimensional data sets is of current interest in many fields. In order to make such analysis operation...
Prequential Plug-In Codes that Achieve Optimal Redundancy Rates even if the Model is Wrong
Prequential Plug-In Codes Optimal Redundancy Rates Model
2010/3/11
We analyse the prequential plug-in codes relative
to one-parameter exponential families M. We show that if data
are sampled i.i.d. from some distribution outside M, then the
redundancy of any plug-...
A Multivariate Variance Components Model for Analysis of Covariance in Designed Experiments
Adjusted mean blocking factor conditionalmodel orthogonal design randomized blocks design.itute of Mathematical Statistics
2010/3/9
Traditional methods for covariate adjustment of treatment
means in designed experiments are inherently conditional on the ob-
served covariate values. In order to develop a coherent general method-
...
Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model
Current status data maximum smoothed likelihood smoothedmaximum likelihood distribution estimation density estimation hazard rate estimation
2010/3/9
We consider the problem of estimating the distribution function,
the density and the hazard rate of the (unobservable) event time in
the current status model. A well studied and natural nonparametri...
Invariant $P$-values for model checking
P-values invariance under transformations discrepancy mea-sures for model checking
2010/3/9
P-values have been the focus of considerable criticism based on
various considerations. Still, the P-value represents one of the most
commonly used statistical tools. When assessing the suitability ...
Bayesian nonparametric analysis for a species sampling model with finitely many types
Bayesian nonparametric analysis species sampling model finitely many types
2010/3/9
We derive explicit Bayesian nonparametric analysis for a species sampling model with
finitely many types of Gibbs form of type = −1 recently introduced in Gnedin (2009).
Our results compleme...
A study of a one - dimensional bilinear differential model for stochastic processes
a one - dimensional bilinear differential model stochastic processes
2009/9/24
A study of a one - dimensional bilinear differential model for stochastic processes。
Prediction of Pregnancy: A Joint Model for Longitudinal and Binary Data
joint model mixed linear model generalized linear model longitudinal data binary data
2009/9/24
We consider the problem of predicting the achievement of successful pregnancy, in a population of women undergoing treatment for infertility, based on longitudinal measurements o...
A Dynamic Modelling Strategy for Bayesian Computer Model Emulation
Computer model emulation Dynamic linear model backward sampling Gaussian process Markov chain Monte Carlo
2009/9/24
Computer model evaluation studies build statistical models of deterministic simulation-based predictions of field data to then assess and criticize the computer model and suggest refinemen...
ABC likelihood-free methods for model choice in Gibbs random fields
Approximate Bayesian Computation model choice Gibbs Random Fields Bayes factor protein folding
2009/9/24
Gibbs random fields (GRF) are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when tho...