1992

最新文章

• SOME INEQUALITIES ABOUT THE KAPLAN-MEIER ESTIMATOR AI翻译
  作者：YANG, S
  摘要：In this paper we consider the product-limit estimator of the survival distribution function in the context of independent but nonidentically distributed censoring times. An upper bound on the mean square increment of the stopped Kaplan-Meier process is obtained. Also, a representation is given for the ratio of the survival distribution function to the product-limit estimator as the product of a bounded process and a martingale. From this representation bounds on the mean square of the ratio an...
• ESTIMATING COEFFICIENT DISTRIBUTIONS IN RANDOM COEFFICIENT REGRESSIONS AI翻译
  作者：BERAN, R; HALL, P
  作者单位：Australian National University
  摘要：Random coefficient regression models are important in representing linear models with heteroscedastic errors and in unifying the study of classical fixed effects and random effects linear models. For prediction intervals and for bootstrapping in random coefficient regressions, it is necessary to estimate the distributions of the random coefficients consistently. We show that this is often possible and provide practical representative estimators of these distributions.
• INDETERMINATE PROBABILITIES ON FINITE SETS AI翻译
  作者：NAU, RF
  摘要：This paper presents a quasi-Bayesian model of subjective uncertainty in which beliefs which are represented by lower and upper probabilities qualified by numerical confidence weights. The representation is derived from a system of axioms of binary preferences which differs from standard axiom systems insofar as completeness is not assumed and transitivity is weakened. Confidence-weighted probabilities may be elicited through the acceptance of bets with limited stakes, a generalization of the o...
• ON BOOTSTRAPPING KERNEL SPECTRAL ESTIMATES AI翻译
  作者：FRANKE, J; HARDLE, W
  作者单位：Universite Catholique Louvain
  摘要：An approach to bootstrapping kernel spectral density estimates is described which is based on resampling from the periodogram of the original data. We show that it is asymptotically valid under suitable conditions, and we illustrate its performance for a medium-sized time series sample with a small simulation study.
• ASYMPTOTIC EXPANSIONS AND BOOTSTRAPPING DISTRIBUTIONS FOR DEPENDENT-VARIABLES - A MARTINGALE APPROACH AI翻译
  作者：MYKLAND, PA
  作者单位：University of California System; University of California Berkeley
  摘要：The paper develops a one-step triangular array asymptotic expansion for continuous martingales which are asymptotically normal. Mixing conditions are not required, but the quadratic variations of the martingales must satisfy a law of large numbers and a central limit type condition. From this result we derive expansions for the distributions of estimators in asymptotically ergodic differential equation models, and also for the bootstrapping estimators of these distributions.
• ON THE LAST TIME AND THE NUMBER OF TIMES AN ESTIMATOR IS MORE THAN EPSILON FROM ITS TARGET VALUE AI翻译
  作者：HJORT, NL; FENSTAD, G
  摘要：Suppose theta(n) is a strongly consistent estimator for theta(0) in some i.i.d. situation. Let N(epsilon) and Q(epsilon) be, respectively, the last n and the total number of n for which theta(n) is at least epsilon away from theta(0). The limit distributions for epsilon(2)N(epsilon) and epsilon(2)Q(epsilon) as epsilon goes to zero are obtained under natural and weak conditions. The theory covers both parametric and nonparametric cases, multidimensional parameters and general distance functions...
• HIGHLY EFFICIENT ESTIMATORS OF MULTIVARIATE LOCATION WITH HIGH BREAKDOWN POINT AI翻译
  作者：LOPUHAA, HP
  摘要：We propose an affine equivariant estimator of multivariate location that combines a high breakdown point and a bounded influence function with high asymptotic efficiency. This proposal is basically a location M-estimator based on the observations obtained after scaling with an affine equivariant high breakdown covariance estimator. The resulting location estimator will inherit the breakdown point of the initial covariance estimator and within the location-covariance model only the M-estimator ...
• BEST POSSIBLE CONSTANT FOR BANDWIDTH SELECTION AI翻译
  作者：FAN, JQ; MARRON, JS
  摘要：For the data based choice of the bandwidth of a kernel density estimator, several methods have recently been proposed which have a very fast asymptotic rate of convergence to the optimal bandwidth. In particular the relative rate of convergence is the square root of the sample size, which is known to be the best possible. The point of this paper is to show how semiparametric arguments can be employed to calculate the best possible constant coefficient, that is, an analog of the usual Fisher in...
• ASYMPTOTIC THEORY FOR NESTED CASE-CONTROL SAMPLING IN THE COX REGRESSION-MODEL AI翻译
  作者：GOLDSTEIN, L; LANGHOLZ, B
  作者单位：University of Southern California
  摘要：By providing a probabilistic model for nested case-control sampling in epidemiologic cohort studies, consistency and asymptotic normality of the maximum partial likelihood estimator of regression parameters in a Cox proportional hazards model can be derived using process and martingale theory as in Andersen and Gill. A general expression for the asymptotic variance is given and used to calculate asymptotic relative efficiencies relative to the full cohort variance in some important special cas...
• INVARIANCE PROPERTIES OF DENSITY RATIO PRIORS AI翻译
  作者：WASSERMAN, L
  摘要：Density ratio neighborhoods are classes of probabilities that are used in robust Bayesian inference. These classes are invariant under Bayesian updating and marginalization. This makes them computationally convenient in robust Bayesian inference. We show that this is the unique class of probabilities that has these invariance properties. Aside from its theoretical value, this result has computational implications as well.