搜索结果: 1-13 共查到“统计学 Importance”相关记录13条 . 查询时间(0.105 秒)
Penalized importance sampling for parameter estimation in stochastic differential equations
Chronic wasting disease Euler-Maruyama scheme Maximum likelihood estimation Partially observed discrete sparse data Penalized importance sampling Stochastic di
2013/6/14
We consider the problem of estimating parameters of stochastic differential equations with discrete-time observations that are either completely or partially observed. The transition density between t...
Metamodel-based importance sampling for structural reliability analysis
reliability analysis importance sampling metamodeling error kriging random fields active learning rare events
2011/6/16
Structural reliability methods aim at computing the probability of failure of systems with
respect to some prescribed performance functions. In modern engineering such functions
usually resort to ru...
The Importance of Scale for Spatial-Confounding Bias and Precision of Spatial Regression Estimators
Epidemiology, identifiability, mixed model,penalized likelihood random effects spatial correlation splines
2010/11/9
Residuals in regression models are often spatially correlated.Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants.
Quantile estimation with adaptive importance sampling
Quantile estimation law of iterated logarithm adaptive im-portance sampling stochastic approximation Robbins–Monro
2010/3/11
We introduce new quantile estimators with adaptive importance
sampling. The adaptive estimators are based on weighted samples
that are neither independent nor identically distributed. Using a
new l...
Theory of Probability is distinguished by several high-level
philosophical attitudes, some stressed by Jeffreys, some implicit. By
reviewing these we may recognize the importance in this work in the...
Importance Re-sampling MCMC for Cross-Validation in Inverse Problems
Cross-validation Inverse Importance Re-sampling Model fit Re-use
2009/9/22
This paper presents a methodology for cross-validation in the context of Bayesian
modelling of situations we loosely refer to as iverse problems It is motivated by
an example from palaeoclimatology ...
A practical illustration of the importance of realistic individualized treatment rules in causal inference
Experimental Treatment Assignment assumption positivity assumption dynamic treatment rules physical activity
2009/9/16
The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks...
Variable importance in binary regression trees and forests
CART random forests maximal subtree
2009/9/16
We characterize and study variable importance (VIMP) and pairwise variable associations in binary regression trees. A key component involves the node mean squared error for a quantity we refer to as a...
Case-deletion importance sampling estimators: Central limit theorems and related results
Infinite Variance Influence Leverage Marginal Residual Sum of Squares Markov Chain Monte Carlo Model Averaging Moment Index Tail Behavior
2009/9/16
Case-deleted analysis is a popular method for evaluating the influence of a subset of cases on inference. The use of Monte Carlo estimation strategies in complicated Bayesian settings leads naturally ...
Estimation of cosmological parameters using adaptive importance sampling
Estimation cosmological parameters adaptive importance sampling
2010/3/19
We present a Bayesian sampling algorithm called adaptive importance sampling or Population
Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to consi...
The importance of being the upper bound in the bivariate family
Hoeffding’s lemmaFrechet-Hoeffding bounds given marginals diagonal expansion logit analysis goodness-of-fit Lorenz curve Bayes test in 2 × 2 tables
2009/2/23
Any bivariate cdf is bounded by the Fr ´echet-Hoeffding lower and upper bounds. We illustrate the importance of the upper bound in several ways. Any bivariate distribution can be written in term...
Importance Tempering
simulated tempering importance sampling Markov chain Monte Carlo(MCMC) Metropolis–coupled MCMC
2010/4/30
Simulated tempering (ST) is an established Markov chain Monte Carlo (MCMC)
method for sampling from a multimodal density (). Typically, ST involves introducing
an auxiliary variable k taking value...
Least Squares Importance Sampling for Monte Carlo Security Pricing
Monte Carlo Simulations Variance Reduction Techniques Importance Sampling Derivatives Pricing
2010/4/27
We describe a simple Importance Sampling strategy for Monte Carlo simulations based on a least
squares optimization procedure. With several numerical examples, we show that such Least Squares Importa...