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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...
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...
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...
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。
Exact confidence intervals for the Hurst parameter of a fractional Brownian motion
Concentration inequalities exact confidence intervals fractional Brownian motion Hurst parameter
2009/9/16
In this short note, we show how to use concentration inequalities in order to build exact confidence intervals for the Hurst parameter associated with a one-dimensional fractional Brownian motion.
Functional asymptotic confidence intervals for a common mean of independent random variables
Lindeberg's condition symmetric random variable Student statistic Student process Wiener process functional central limit theorem
2009/9/16
We consider independent random variables (r.v.'s) with a common mean $mu$ that either satisfy Lindeberg's condition, or are symmetric around $mu$. Present forms of existing functional central limit th...
Asymptotic Confidence Intervals Based on M-procedures in One- and Two-sample Models
asymptotics confidence region M-estimators robustness
2009/3/9
Asymptotic confidence intervals of location parameters are proposed in one- and two-sample models. These are robust procedures based on scale-invariant M-statistics. The one-sample procedures have the...
AsympTest: an R package for performing parametric statistical tests and confidence intervals based on the central limit theorem
parametric tests and confidence intervals central limit theorem R package
2010/3/18
This paper describes an R package implementing large sample tests and confidence
intervals (based on the central limit theorem) for various parameters. The one and
two sample mean and variance conte...