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Provably Safe and Robust Learning-Based Model Predictive Control
Safe and Robust Learning-Based Model Predictive Control
2011/7/19
Controller design for systems typically faces a trade-off between robustness and performance, and the reliability of linear controllers has caused many control practitioners to focus on the former. Ho...
A context dependent pair hidden Markov model for statistical alignment
Comparative genomics Contextual alignment DNA sequence alignment EM algorithm
2011/7/19
This article proposes a novel approach to statistical alignment of nucleotide sequences by introducing a context dependent structure on the substitution process in the underlying evolutionary model. W...
Application of Predictive Model Selection to Coupled Models
Predictive Model Selection Quantity of In-terest Model Validation Decision Making
2011/7/19
A predictive Bayesian model selection approach is presented to discriminate coupled models used to predict an unobserved quantity of interest (QoI).
Model selection by LASSO methods in a change-point model
change-points selection criterion asymptotic behavior
2011/7/19
The paper considers a linear regression model with multiple change-points occurring at unknown times.
Change point analysis of an exponential model based on Phi-divergence test-statistics: simulated critical points case
Change poin Exponential model Likelihood ratio test
2011/7/19
Recently Batsidis \textit{et al.} (2011) have presented a new procedure based on divergence measures for testing the hypothesis of the existence of a change point in exponential populations.
Application of Bayesian model inadequacy criterion for multiple data sets to radial velocity models of exoplanet systems
Methods Statistical – Techniques Radial velocities – Stars Individual: GJ 581 HD 217107 υ Andromedae
2011/7/7
We present a simple mathematical criterion for determining whether a given statistical model does not describe several independent sets of measurements, or data modes, adequately. We derive this crite...
A Dirty Model for Multiple Sparse Regression
Terms—Multi-task Learning High-dimensional Statistics
2011/7/7
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sp...
Considerate Approaches to Achieving Sufficiency for ABC model selection
Considerate Approaches Achieving Sufficiency ABC model selection
2011/7/6
For nearly any challenging scientific problem evaluation of the likelihood is problematic if not impossible. Approximate Bayesian computation (ABC) allows us to employ the whole Bayesian formalism to ...
Monte Carlo algorithms for model assessment via conflicting summaries
Metropolis-Hastings Sequential Monte Carlo model choice
2011/7/6
The development of statistical methods and numerical algorithms for model choice is vital to many real-world applications. In practice, the ABC approach can be instrumental for sequential model design...
Nonparametric Estimation of Second-Order Jump-Diffusion Model
Second-order jump-diffusion N-W estimator Weak consistency
2011/7/6
We study the nonparametric estimators of the infinitesimal coefficients of the second-order jump-diffusion models. Under the mild conditions, we obtain the weak consistency and the asymptotic normalit...
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization, Sparsity
2011/7/6
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a...
Adaptive estimation in the nonparametric random coefficients binary choice model by needlet thresholding
Discrete choice models random coefficients inverse problems
2011/7/6
In this article we consider the estimation of the joint distribution of the random coefficients and error term in the nonparametric random coefficients binary choice model. In this model from economic...
A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix
Correlated factors Inverse Wishart Markov chain Monte Carlo
2011/7/5
Our article considers a regression model with observed factors. The observed factors have a flexible stochastic volatility structure that has separate dynamics for the volatilities and the correlation...
spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
MCMC P-splines spike-and-slab prior normal-inverse-gamma
2011/6/20
The R package spikeSlabGAM implements Bayesian variable selection, model choice,
and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and
Poisson responses. Its purpose ...
Consistent Model Selection of Discrete Bayesian Networks from Incomplete Data
Discrete Bayesian Networks Consistent Model Incomplete Data node-variables
2011/6/20
A maximum likelihood based model selection of discrete Bayesian
networks is considered. The model selection is performed through scoring
function S, which, for a given network G and n-sample Dn, is ...