Improved Monte Carlo inference for models with additive error

Some statistical models defined in terms of a generating stochastic mechanism have intractable distribution theory, which renders parameter estimation difficult. However, a Monte Carlo estimate of the log-likelihood surface for such a model can be obtained via computation of nonparametric density estimates from simulated realizations of the model. Unfortunately, the bias inherent in density estimation can cause bias in the resulting log-likelihood estimate that alters the location of its maximizer. In this paper a methodology for radically reducing this bias is developed for models with an additive error component. An illustrative example involving a stochastic model of molecular fragmentation and measurement is given.     

Confidence Intervals for Current Status Data

Abstract.  The likelihood ratio statistic for testing pointwise hypotheses about the survival time distribution in the current status model can be inverted to yield confidence intervals (CIs). One advantage of this procedure is that CIs can be formed without estimating the unknown parameters that figure in the asymptotic distribution of the maximum likelihood estimator (MLE) of the distribution function. We discuss the likelihood ratio-based CIs for the distribution function and the quantile function and compare these intervals to several different intervals based on the MLE. The quantiles of the limiting distribution of the MLE are estimated using various methods including parametric fitting, kernel smoothing and subsampling techniques. Comparisons are carried out both for simulated data and on a data set involving time to immunization against rubella. The comparisons indicate that the likelihood ratio-based intervals are preferable from several perspectives.     

Pathways between education and health: a causal modelling approach

Summary.  The association of poor education and poor health has been consistently observed in many studies and in various countries. Thus far, studies examining the mechanisms underlying this association have looked at only a limited set of potential pathways. This study simultaneously examines six distinctive pathways, which have been hypothesized to link education and health and found support from previous studies. A causal analysis of education and health was performed using structural equation models. Data were used from six phases of the National Child Development Study, which is based on following up an initial sample of 17416 children who were born in 1958. The association between education and health appears to be explained by a combination of mechanisms: adolescent health and adult health behaviours for men and women, adult social class among men and parental social class among women. We conclude that improvements in population educational attainment may not automatically lead to improvements in population health, and that health policies for improving health and reducing health inequalities need to target specific causal pathways.     

一类变系数高阶线性齐次微分方程解的探求(英文)

本文对系数全为多项式和广义多项式的n阶线性齐次微分方程引入特征方程的概念。给出了具有指数型解的充要条件，推广了经典的常系数线性方程和著名的Euler方程的的解法，为求解变系数线性微分方程提供了有效的方法。     

非线性热传导方程u_t-Δu=D~βf(u)的Cauchy问题的整体解及渐近性

本文利用压缩映象原理及线性方程 Cauchy 问题的解的衰减性佶计,研究了一类非线性热传导方程 Cauchy 问题的整体解的存在唯一性及渐近性,得到当初值及非线性项满足一定条件之下Cauchy 问题的适当性。从而补充了文[3]的结果,首次指出了文[1]的结果是对半线性方程独有的。     

Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

A simple,automatic and adaptive bivariate density estimator based on conditional densities

The standard approach to non-parametric bivariate density estimation is to use a kernel density estimator. Practical performance of this estimator is hindered by the fact that the estimator is not adaptive (in the sense that the level of smoothing is not sensitive to local properties of the density). In this paper a simple, automatic and adaptive bivariate density estimator is proposed based on the estimation of marginal and conditional densities. Asymptotic properties of the estimator are examined, and guidance to practical application of the method is given. Application to two examples illustrates the usefulness of the estimator as an exploratory tool, particularly in situations where the local behaviour of the density varies widely. The proposed estimator is also appropriate for use as a pilot estimate for an adaptive kernel estimate, since it is relatively inexpensive to calculate.     

一类二阶中立型时滞差分方程的振动定理

研究了一类具有变系数的二阶中立型时滞差分方程的振动性,得到了该类方程振动及差分算子△振动的一个充分条件.     

一类具耗散的Schrodinger方程的整体吸引子的存在性

本文对一类具耗散的Schrodinger方程的整体吸引子的存在性问题进行了研究.研究表明:这类系统在特定的抽象空间中存在整体吸引子.     

多元正态分布广义方差区间估计的改进

讨论了多元正态分布广义方差的区间估计问题,给出了在覆盖率及长度上均优于最优仿射同变区间估计的改进估计．