Výsledky vyhledávání - "Statistics and Computing/Statistics Programs"

Monte Carlo statistical methods Autor Robert, Christian P., Casella, George

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De-noising boosting methods for variable selection and estimation subject to error-prone variables Autor Chen, Li-Pang

“…Boosting is one of the most powerful statistical learning methods that combines multiple weak learners into a strong learner. The main idea of boosting is to…”
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Brainpack: a suite of advanced statistical techniques for multi-subject and multi-group fMRI data analysis Autor Samaddar, Arunava, Jackson, Brooke S., Lazar, Nicole A., McDowell, Jennifer E., Park, Cheolwoo

“… Also, researchers are interested in drawing conclusions from a sample of subjects to a relevant population, and in comparing the performance between groups of people…”
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Double debiased estimation and inference for longitudinal generalized linear models with hidden confounders: Double debiased estimation and inference Autor Fei, Yuxin, Liu, Jin, Wang, Lei

“…The hidden confounding model has been widely applied in many fields. Without adjusting for the hidden confounders, the estimators from the standard methods of…”
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Penalized empirical likelihood for longitudinal expectile regression with growing dimensional data Autor Zhang, Ting, Wang, Yanan, Wang, Lei

“…Expectile regression (ER) naturally extends the classical least squares to investigate heterogeneous effects of covariates on the distribution of the response…”
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Linear quantile mixed models Autor Geraci, Marco, Bottai, Matteo

“… In a previous publication (Geraci and Bottai in Biostatistics 8:140–154, 2007 ), we proposed a conditional quantile regression model for continuous responses where subject…”
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Bayesian ODE solvers: the maximum a posteriori estimate Autor Tronarp, Filip, Särkkä, Simo, Hennig, Philipp

“… . Subject to mild conditions on the vector field, convergence rates of the maximum a posteriori estimate are then obtained via methods from nonlinear analysis and scattered data approximation. These results closely resemble classical convergence results in the sense that a ν times differentiable prior process obtains a global order of ν , which is demonstrated in numerical examples…”
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Unbalanced Longitudinal Data Clustering with a Copula Kernel Mixture Model: Unbalanced Longitudinal Data Clustering Autor Zhang, Xi, Murphy, Orla A., McNicholas, Paul D.

“…Unbalanced longitudinal data appears commonly in practice, for example in cases where measurements are collected at different time points for different subjects and can therefore be sparse and/or irregularly sampled…”
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Heterogeneous Analysis of Longitudinal Profiles using Adaptive Banded Precision Matrices via Penalized Fusion: Heterogeneous Analysis of Longitudinal Profiles using Adaptive Autor Liang, Chunhui, Ma, Wenqing

“… In this paper, we assume that subjects are sampled from a Gaussian mixture distribution, and incorporate regularized bandable precision matrix structure information for each subgroup…”
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Efficient importance sampling for large sums of independent and identically distributed random variables Autor Ben Rached, Nadhir, Haji-Ali, Abdul-Lateef, Rubino, Gerardo, Tempone, Raúl

“…We discuss estimating the probability that the sum of nonnegative independent and identically distributed random variables falls below a given threshold, i.e.,…”
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Properties of the stochastic approximation EM algorithm with mini-batch sampling Autor Kuhn, Estelle, Matias, Catherine, Rebafka, Tabea

“…To deal with very large datasets a mini-batch version of the Monte Carlo Markov Chain Stochastic Approximation Expectation–Maximization algorithm for general…”
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Bayesian analysis of Box-Cox transformation model for multi-state progression-free survival data Autor Wang, Chunjie, Qi, Shunxin, Jiang, Jingjing

“…In this study, we address the inference problem associated with the Box-Cox transformation model when dealing with multi-state progression-free survival data…”
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Quantile regression for longitudinal data based on latent Markov subject-specific parameters Autor Farcomeni, Alessio

“…We propose a latent Markov quantile regression model for longitudinal data with non-informative drop-out. The observations, conditionally on covariates, are…”
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Nonparametric empirical bayes prediction in mixed models: Nonparametric empirical bayes prediction in mixed models Autor Banerjee, Trambak, Sharma, Padma

“…Mixed models are classical tools in statistics for modeling repeated data on subjects, such as data on patients or firms collected over time…”
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Predictive Subgroup Logistic Regression for Classification with Unobserved Heterogeneity Autor Chen, Kun, Huang, Rui, Tong, Zhiwei

“…Unobserved heterogeneity refers to the variation among subjects that is not accounted for by the observed features used in a model…”
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Improved multiple quantile regression estimation with nonignorable dropouts Autor Ma, Wei, Zhang, Ting, Wang, Lei

“… The relationship between the multiple quantiles and within-subject correlation is accommodated to improve efficiency in the presence of nonignorable dropouts…”
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Accelerated failure time models with error-prone response and nonlinear covariates Autor Chen, Li-Pang

“… However, gene expressions are possibly nonlinear and the survival time as well as censoring status are subject to measurement error…”
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Mixture cure semiparametric additive hazard models under partly interval censoring — a penalized likelihood approach Autor Li, Jinqing, Ma, Jun

“…Survival analysis can sometimes involve individuals who will not experience the event of interest, forming what is known as the “cured group”. Identifying such…”
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A Bayesian Model for Co-clustering Ordinal Data with Informative Missing Entries Autor Giampino, Alice, Canale, Antonio, Nipoti, Bernardo

“… Our model exploits the flexibility of two independent Dirichlet processes, allowing us to infer potentially distinct subpopulations that characterize the latent structure of both subjects and variables…”
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Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification Autor Titman, Andrew C.

“…The paper considers non-parametric maximum likelihood estimation of the failure time distribution for interval-censored data subject to misclassification…”
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