论文检索

Bernstein 多项式密度估计的收敛速度AI 解读

Mixture models for density estimation provide a very useful set up for the Bayesian or the maximum likelihood approach.For a density on the unit interval, mixtures of beta densities form a flexible model. The class of Bernstein densities is a much smaller subclass of the beta mixtures defined by Bernstein polynomials, which can approximate any continuous density. A Bernstein polynomial prior is obtained by putting a prior distribution on the class of Bernstein densities. The…

不假定参数化误差分布的 Box–Cox 变换模型估计AI 解读

Box and Cox proposed a power transformation for the response variable that yields a linear regression model with normal error and constant variance. Inference procedures for the regression coefficients and transformation parameter under this model setting have been studied extensively. In this article we propose a simple semiparametric estimation method for the Box–Cox transformation model with no specific parametric assumption on the distribution of the error term. The…

结肠远端与近端致癌物诱导 DNA 加合物水平关系的参数与非参数方法AI 解读

An important problem in studying the etiology of colon cancer is understanding the relationship between DNA adduct levels (broadly, DNA damage) in cells within colonic crypts in distal and proximal parts of the colon, following treatment with a carcinogen and different types of diet. In particular, it is important to understand whether rats who have elevated adduct levels in particular positions in distal region crypts also have elevated levels in the same positions of the…

含线性时间趋势的自回归模型中的子抽样置信区间AI 解读

A new method is proposed for constructing confidence intervals in autoregressive models with linear time trend. Interest focuses on the sum of the autoregressive coefficients because this parameter provides a useful scalar measure of the long-run persistence properties of an economic time series. Since the type of the limiting distribution of the corresponding OLS estimator, as well as the rate of its convergence, depend in a discontinuous fashion upon whether the true…

标记点过程统计量的矩估计AI 解读

Summary In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and we establish their L 2-consistency. The variance estimator in particular can be used for the construction of confidence intervals for estimated…

摘要暂缺

平滑中位数与自助法AI 解读

Even in one dimension the sample median exhibits very poor performance when used in conjunction with the bootstrap. For example, both the percentile‐t bootstrap and the calibrated percentile method fail to give second‐order accuracy when applied to the median. The situation is generally similar for other rank‐based methods, particularly in more than one dimension. Some of these problems can be overcome by smoothing, but that usually requires explicit choice of the smoothing…

通过扰动优化目标进行简单重抽样AI 解读

Suppose that under a semiparametric setting an estimator of a vector of parameters of interest is obtained by optimising an objective function which has a U‐process structure. The covariance matrix of the estimator is generally a function of the underlying density function, which may be difficult to estimate well by conventional methods. In this paper, we present a simple resampling method by perturbing the objective function repeatedly. Inferences of the parameters can then…

M估计中标量参数剖面经验似然函数的近似AI 解读

Empirical likelihood possesses many of the important properties of genuine parametric likelihood, but it is computationally burdensome, especially when nuisance parameters are present. This paper presents two approximations to the profile empirical likelihood function for a scalar parameter of interest in the context of M‐estimation; the simpler approximation is based on the third derivative of the profile log empirical likelihood function at its maximising point, while the…

摘要暂缺

时间序列非参数估计的马尔可夫局部重抽样方案AI 解读

In this paper we study the properties of a pth-order Markovian local resampling procedure in approximating the distribution of nonparametric (kernel) estimators of the conditional expectation m(x;φ). Under certain regularity conditions, asymptotic validity of the proposed resampling scheme is established for a class of stochastic processes that is broader than the class of stationary Markov processes. Some simulations illustrate the finite sample performance of the proposed…

时间序列回归模型中条件异方差的一致检验AI 解读

We show that the standard consistent test for testing the null of conditional homoskedasticity (against conditional heteroskedasticity) can be generalized to a time-series regression model with weakly dependent data and with generated regressors. The test statistic is shown to have an asymptotic normal distribution under the null hypothesis of conditional homoskedastic error. We also discuss extension of our test to the case of testing the null of a parametrically specified…

针对光滑备择的广义线性与半参数模型检验AI 解读

Summary We propose goodness-of-fit tests for testing generalized linear models and semiparametric regression models against smooth alternatives. The focus is on models having both continous and factorial covariates. As a smooth extension of a parametric or semiparametric model we use generalized varying-coefficient models as proposed by Hastie and Tibshirani. A likelihood ratio statistic is used for testing. Asymptotic expansions allow us to write the estimates as linear…

追踪移动目标：动态贝叶斯模型的蒙特卡洛推断AI 解读

Summary Markov chain Monte Carlo (MCMC) sampling is a numerically intensive simulation technique which has greatly improved the practicality of Bayesian inference and prediction. However, MCMC sampling is too slow to be of practical use in problems involving a large number of posterior (target) distributions, as in dynamic modelling and predictive model selection. Alternative simulation techniques for tracking moving target distributions, known as particle filters, which…

摘要暂缺

自回归模型的自助法后再自助法预测区间AI 解读

The use of the Bonferroni prediction interval based on the bootstrap-after-bootstrap is proposed for autoregressive (AR) models. Monte Carlo simulations are conducted using a number of AR models including stationary, unit-root, and near-unit-root processes. The major finding is that the bootstrap-after-bootstrap provides a superior small-sample alternative to asymptotic and standard bootstrap prediction intervals. The latter are often too narrow, substantially…

协整向量线性约束的 Bootstrap 检验AI 解读

We consider a computer-intensive method for inference on cointegrating vectors in maximum likelihood cointegration analysis. Simulation studies show that the size distortion for the asymptotic likelihood ratio test can be considerable for small samples. Itis demonstrated that a parametric bootstrap frequently results in a nearly exact α-level test. Furthermore, response surface regression is used to examine small-sample properties of the asymptotic test. In particular, using…

多元分布的非参数蒙特卡洛检验AI 解读

In this paper, nonparametric Monte Carlo test procedures are proposed for composite hypotheses in which the multivariate distribution belongs to a nonparametric family. When a random variable is, in distribution, equal to a product of two independent variables, Monte Carlo simulations can be performed conditionally on one of the variables. They can be applied to testing several commonly used distributions including elliptically symmetric, reflectively symmetric,…

利用定性约束的 bootstrap 方法降低变异性AI 解读

Parametric methods are often attractive for small to moderate samples, since, relative to nonparametric techniques, they reduce variability of a wide range of statistical procedures. However, when sample size is small there is often little empirical evidence to support a particular model, and so, while variance can be reduced, bias may be increased. In theory, smoothing can be used to reduce variability of nonparametric procedures, but the difficulty of choosing the…

异方差下线性自回归动态的检验AI 解读

A puzzling characteristic of asset returns for various frequencies is the often observed positive autocorrelation at lag one. To some extent this can be explained by standard asset pricing models when assuming time-varying risk premia. However, one often finds better results when directly fitting an autoregressive model, for which there is little economic foundation. One may ask whether the underlying process does in fact contain an autoregressive component. It is therefore…

摘要暂缺

摘要暂缺

确定性模拟模型的推断：贝叶斯融合方法AI 解读

Deterministic simulation models are used in many areas of science, engineering, and policy making. Typically, these are complex models that attempt to capture underlying mechanisms in considerable detail, and they have many user-specified inputs. The inputs are often specified by some form of trial-and-error approach in which plausible values are postulated, the corresponding outputs inspected, and the inputs modified until plausible outputs are obtained. Here we address the…

摘要暂缺