杨赤中滤波推估法在土石方量计算中的应用

应用杨赤中滤波推估法 ,将地形测图中得到的地形碎部点高程观测值看作一种包含规律性变化和随机性变化的复合变量 ,利用二项系数加权游动平均对观测值序列进行逐遍滤波 ,显现出基本变化成分 ,建立估值数学模型 ,求得各个方格中心高程估值。根据工程建设的要求 ,使估网座标横轴方向与坡度方向相同 ,则可按估网列数求出各方格中心的设计高程 ,从而求得各方格的施工标高 ,算得土石方量并统计汇总。

基于杨赤中推估法的DEM及等值线绘制

数字高程模型(DEM)是GIS最重要的组成部分,应用杨赤中滤波推估法,可将离散的高程数据经插值计算转换为格网DEM数据。在此基础上,利用VB和MapX实现等值线绘制功能。详细讨论杨赤中推估法构造DEM的原理和实现绘制等值线功能的步骤。

应用杨赤中插值原理构造GIS的DEM模型

数字高程模型(DEM)是GIS最重要的组成部分 ,应用杨赤中滤波推估法 ,可将离散的DEM数据经插值计算转换为格网DEM数据。其大致过程是 :将地形测图中得到的地形碎部点高程观测值看作一种包含规律性变化和随机性变化的复合变量 ,利用二项系数加权游动平均对观测值序列进行逐遍滤波 ,显现出基本变化成分 ,建立估值数学模型 。

Robust range-parameterized cubature Kalman filter for bearings-only tracking

In order to improve tracking accuracy when initial estimate is inaccurate or outliers exist,a bearings-only tracking approach called the robust range-parameterized cubature Kalman filter(RRPCKF)was proposed.Firstly,the robust extremal rule based on the pollution distribution was introduced to the cubature Kalman filter(CKF)framework.The improved Turkey weight function was subsequently constructed to identify the outliers whose weights were reduced by establishing equivalent innovation covariance matrix in the CKF.Furthermore,the improved range-parameterize(RP)strategy which divides the filter into some weighted robust CKFs each with a different initial estimate was utilized to solve the fuzzy initial estimation problem efficiently.Simulations show that the result of the RRPCKF is more accurate and more robust whether outliers exist or not,whereas that of the conventional algorithms becomes distorted seriously when outliers appear.

Joint semiparametric mean-covariance model in longitudinal study

Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decompose the covariance structure. Then the covariance structure is fitted by a semiparametric model by imposing parametric within-subject correlation while allowing the nonparametric variation function. We estimate regression functions by using the local linear technique and propose generalized estimating equations for the mean and correlation parameter. Kernel estimators are developed for the estimation of the nonparametric variation function. Asymptotic normality of the the resulting estimators is established. Finally, the simulation study and the real data analysis are used to illustrate the proposed approach.

Some conditional results for conditionally strong mixing sequences of random variables

The relation between strong mixing and conditionally strong mixing is answered by examples,that is,the strong mixing property of random variables does not imply the conditionally strong mixing property,and the opposite implication is also not true.Some equivalent definitions and basic properties of conditional strong mixing random variables are derived,and several conditional covariance inequalities are obtained.By means of these properties and conditional covariance inequalities,a conditional central limit theorem stated in terms of conditional characteristic functions is established,which is a conditional version of the earlier result under non-conditional case.

Almost Sure Asymptotics for Extremes of Non-stationary Gaussian Random Fields

In this paper, the authors prove an almost sure limit theorem for the maxima of non-stationary Caussian random fields under some mild conditions related to the covariance functions of the Gaussian fields. As the by-products, the authors also obtain several weak convergence results which extended the existing results.

Model-independent determination on H_0 using the latest cosmic chronometer data

We perform the updated constraints on the Hubble constant H_0 by using the model-independent method, Gaussian processes.Utilizing the latest 30 cosmic chronometer measurements, we obtain H_0= 67.38 ± 4.72 km s^(-1)Mpc^(-1), which is consistent with the Planck 2015 and Riess et al. analysis at 1σ confidence level. Different from the results of Busti et al. by only using 19 H(z) measurements, our reconstruction results of H(z) and the derived values of H_0 are insensitive to the concrete choice of covariance functions of Matern family.

Empirical likelihood for right censored data with covariables

For complete observation and p-dimensional parameter θ defined by an estimation equation, empirical likelihood method of construction of confidence region is based on the asymptotic χp2 distribution of -2 log（EL ratio）. For right censored lifetime data with covariables, however, it is shown in literature that -2 log（EL ratio） converges weakly to a scaled χp2 distribution, where the scale parameter is a function of unknown asymptotic covariance matrix. The construction of confidence region requires estimation of this scale parameter. In this paper, by using influence functions in the estimating equation, we show that -2 log（EL ratio） converges weakly to a standard χp2 distribution and hence eliminates the procedure of estimating the scale parameter.

QUADRATIC ADMISSIBLE ESTIMATE OF COVARIANCE IN PSEUDO-ELLIPTICAL CONTOURED DISTRIBUTION

This article mainly discusses the admissibility of quadratic estimate of covariance in pseudoelliptical distribution. Under the quadratic loss function, the necessary and sufficient conditions that a quadratic estimator is an admissible estimator of covariance in the class of quadratic estimators are obtained. A complete class of the quadratic estimator class is also given.

A simple construction of optimal estimation in multivariate marginal Cox regression

The multivariate extension of the Cox model proposed by Wei,Lin and Weissfeld in 1989 has been widely used for analyzing multivariate survival data.Under the model assumption,failure times from an individual are assumed to marginally follow their respective proportional hazards regression relation,leaving the joint distribution completely unspecified.This paper presents a simple approach to efficiency improvement through segmentation of stochastic integrals in the marginal estimating equations and incorporation of the limiting covariance structure.It is shown that when partition of the time interval is done at a suitable rate,the resulting estimator is consistent and asymptotically normal.Through the reproducing kernel Hilbert space arising from the covariance function of the limiting Gaussian process,it is also shown that the proposed estimator is asymptotically optimal within a reasonable class of estimators under marginal specification.Simulations are conducted to assess the finite-sample performance of the proposed method.