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Confidence Intervals for Random Forests:The Jackknife and the Infinitesimal Jackknife
bagging jackknife methods Monte Carlo noise variance estimation
2015/8/21
We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging propos...
We study precise conditions under which the cyclic regenerative confidence intervals of Sargent and Shanthikumar are asymptotically valid. We also obtain an optimal way of implementing the cyclic rege...
Coverage Error for Confidence Intervals Arising in Simulation Output Analysis
Coverage Error Confidence Intervals Arising Simulation Output Analysis
2015/7/8
Coverage error asymptotics for confidence intervals arising in simulation are discussed~ Asymptotic expansions, to order O(n-1) (n is the sample size), are given for confidence intervals associated wi...
Estimation of Continuous-time Markov Processes Sampled at Random Time Intervals
Method of moments parameter estimation Markov process
2015/7/6
We introduce a family of generalized-method-of-moments estimators of the parameters of a continuous-time Markov process observed at random time intervals. The results include strong consistency, asymp...
Asymptotic Validity of Batch Means Steady-State Confidence Intervals
Asymptotic Validity Batch Means Steady-State Confidence Intervals
2015/7/6
Themethod of batch means is a widely applied procedure for constructing steady-state confidence intervals. The traditional theoretical support for the method of batch means has rested on the assumptio...
Adaptive confidence intervals for regression functions under shape constraints
Adaptation confidence interval convex function coverage probability expected length minimax estimation modulus of continuity monotone func-tion nonparametric regression shape constraint white noise model
2013/6/14
Adaptive confidence intervals for regression functions are constructed under shape constraints of monotonicity and convexity. A natural benchmark is established for the minimum expected length of conf...
On confidence intervals in regression that utilize uncertain prior information about a vector parameter
Frequentist confidence interval Prior information Linear regression
2013/4/28
Consider a linear regression model with n-dimensional response vector, p-dimensional regression parameter beta and independent normally distributed errors. Suppose that the parameter of interest is th...
The cost of using exact confidence intervals for a binomial proportion
Asymptotic expansion binomial distribution expected length sample size determination proportion
2013/4/27
When computing a confidence interval for a binomial proportion p one must choose between using an exact interval, which has a coverage probability of at least 1-{\alpha} for all values of p, and a sho...
Adaptive Markov Chain Monte Carlo confidence intervals
Adaptive Markov Chain Monte Carlo confidence intervals
2012/11/22
In Adaptive Markov Chain Monte Carlo (AMCMC) simulation, classical estimators of asymptotic variances are inconsistent in general. In this work we establish that despite this inconsistency, confidence...
Guaranteed Conservative Fixed Width Confidence Intervals Via Monte Carlo Sampling
Guaranteed Conservative Fixed Width Confidence Intervals Monte Carlo Sampling
2012/9/17
Monte Carlo methods are used to approximate the means,? of random variablesY, whose distributions are not known explicitly. The key idea is that the
average of a random sample,Y1,...,Yn, tends to 礱sn...
A Normal Hierarchical Model and Minimum Contrast Estimation for Random Intervals
random intervals Normality hierarchical Choquet functional minimum contrast estimator strong consistency asymptotic normality.
2012/9/19
Many statistical data are imprecise due to factors such as mea-surement errors, computation errors, and lack of information. In such cases, data are better represented by intervals rather thanby singl...
Confidence intervals for sensitivity indices using reduced-basis metamodels
sensitivity analysis reduced basis method Sobol indices bootstrap method Monte Carlo method
2011/3/24
Global sensitivity analysis is often impracticable for complex and time demanding numerical models, as it requires a large number of runs. The reduced-basis approach provides a way to replace the orig...
Chi-square Intervals for a Poisson Parameter - Bayes, Classical and Structural
Confidence interval coverage probability estimation interval Poisson
2011/3/18
The 'standard' confidence interval for a Poisson parameter is only one of a number of estimation intervals based on the chi-square distribution that may be used in the estimation of the mean or mean r...
Asymptotic multivariate normality for the subseries values of a general statistic form a stationary sequence - with applications to nonparametric confidence intervals
Asymptotic multivariate the subseries values of a general statistic a stationary sequence
2009/9/23
Asymptotic multivariate normality for the subseries values of a general statistic form a stationary sequence - with applications to nonparametric confidence intervals。
ON THE CONSTRUCTION AND PROPERTIES OF BOOTSTRAP-t PREDICTION INTERVALS FOR STATIONARY TIME SERIES
Prediction intervals sieve bootstrap-t method of sieves
2009/9/18
We consider the construction of unconditional bootstrap-
t prediction intervals for stationary time series. Our approach
relies on the sieve bootstrap resampling scheme introduced by
Biihlmann.
Ba...