两步纯追踪航向校正模型算法

为了提高农业机械导航精度,在纯追踪算法的基础上,设计了两步纯追踪航向校正模型算法,通过校正车体的航 向角,减小车体的横向误差。在MATLAB环境下仿真结果表明,该算法比纯追踪算法的精度高,最大误差和标准差均有 所减小。在4轮电动车上,搭建液压系统和卫星接收系统,进行现场试验,验证了两步纯追踪航向校正模型算法的可行 性和有效性。     

Functional calibration estimation by the maximum entropy on the mean principle

We extend the problem of obtaining an estimator for the finite population mean parameter incorporating complete auxiliary information through calibration estimation in survey sampling under a functional data framework. The functional calibration sampling weights of the estimator are obtained by matching the calibration estimation problem with the maximum entropy on the mean – MEM – principle. In particular, the calibration estimation is viewed as an infinite-dimensional linear inverse problem following the structure of the MEM approach. We give a precise theoretical setting and estimate the functional calibration weights assuming, as prior measures, the centred Gaussian and compound Poisson random measures. Additionally, through a simple simulation study, we show that the proposed functional calibration estimator improves its accuracy compared with the Horvitz–Thompson one.     

SAMPLING AND ESTIMATION IN THE PRESENCE OF CUT-OFF SAMPLING

Cut-off sampling consists of deliberately excluding a set of units from possible selection in a sample, for example if the contribution of the excluded units to the total is small or if the inclusion of these units in the sample involves high costs. If the characteristics of interest of the excluded units differ from those of the rest of the population, the use of naïve estimators may result in highly biased estimates. In this paper, we discuss the use of auxiliary information to reduce the bias by means of calibration and balanced sampling. We show that the use of the available auxiliary information related to both the variable of interest and the probability of being excluded enables us to reduce the potential bias. A short numerical study supports our findings.     

Bias-calibrated estimation from sample surveys containing outliers

We discuss the problem of estimating finite population parameters on the basis of a sample containing representative outliers. We clarify the motivation for Chambers's bias-calibrated estimator of the population total and show that bias calibration is a key idea in constructing estimators of finite population parameters. We then link the problem of estimating the population total to distribution function or quantile estimation and explore a methodology based on the use of Chambers's estimator. We also propose methodology based on the use of robust estimates and a bias-calibrated form of the Chambers and Dunstan estimator of the population distribution function. This proposal leads to a bias-calibrated estimator of the population total which is an alternative to that of Chambers. We present a small simulation study to illustrate the utility of these estimators.     

Exit polling in a cold climate: the BBC–ITV experience in Britain in 2005

Summary.  Conducting an exit poll to forecast the outcome of a national election in terms of both votes and seats is particularly difficult in Britain. No official information is available on how individual polling stations voted in the past, use of single-member plurality means that there is no consistent relationship between votes and seats, electors can choose to vote by post and most of those who vote in person do so late in the day. In addition, around one in every six intended exit poll respondents refuses to participate. Methods that were developed to overcome these problems, and their use in the successful 2005 British Broadcasting Corporation–Independent Television exit poll, are described and evaluated. The methodology included a panel design to allow the estimation of electoral change at local level, coherent multiple-regression modelling of multiparty electoral change to capture systematic patterns of variation, probabilistic prediction of constituency winners to account for uncertainty in projected constituency level shares, collection of information about the voting intentions of postal voters before polling day and access to interviewer guesses on the voting behaviour of refusals. The coverage and accuracy of the exit poll data are critically examined, the effect of key aspects of the statistical modelling of the data is assessed and some general lessons are drawn for the design and analysis of electoral exit polls.     

Identifying Nonlinear Relationships in Regression using the ACE Algorithm

This paper introduces an alternating conditional expectation (ACE) algorithm: a non-parametric approach for estimating the transformations that lead to the maximal multiple correlation of a response and a set of independent variables in regression and correlation analysis. These transformations can give the data analyst insight into the relationships between these variables so that this can be best described and non-linear relationships uncovered. Using the Bayesian information criterion (BIC), we show how to find the best closed-form approximations for the optimal ACE transformations. By means of ACE and BIC, the model fit can be considerably improved compared with the conventional linear model as demonstrated in the two simulated and two real datasets in this paper.     

Optimal bandwidth matrices in functional principal component analysis of density functions

In order to explore and compare a finite number T of data sets by applying functional principal component analysis (FPCA) to the T associated probability density functions, we estimate these density functions by using the multivariate kernel method. The data set sizes being fixed, we study the behaviour of this FPCA under the assumption that all the bandwidth matrices used in the estimation of densities are proportional to a common parameter h and proportional to either the variance matrices or the identity matrix. In this context, we propose a selection criterion of the parameter h which depends only on the data and the FPCA method. Then, on simulated examples, we compare the quality of approximation of the FPCA when the bandwidth matrices are selected using either the previous criterion or two other classical bandwidth selection methods, that is, a plug-in or a cross-validation method.     

Estimating Noncancer Uncertainty Factors: Are Ratios NOAELs Informative?

The prominent role of animal bioassay evidence in environmental regulatory decisions compels a careful characterization of extrapolation uncertainties. In noncancer risk assessment, uncertainty factors are incorporated to account for each of several extrapolations required to convert a bioassay outcome into a putative subthreshold dose for humans. Measures of relative toxicity taken between different dosing regimens, different endpoints, or different species serve as a reference for establishing the uncertainty factors. Ratios of no observed adverse effect levels (NOAELs) have been used for this purpose; statistical summaries of such ratios across sets of chemicals are widely used to guide the setting of uncertainty factors. Given the poor statistical properties of NOAELs, the informativeness of these summary statistics is open to question. To evaluate this, we develop an approach to calibrate the ability of NOAEL ratios to reveal true properties of a specified distribution for relative toxicity. A priority of this analysis is to account for dependencies of NOAEL ratios on experimental design and other exogenous factors. Our analysis of NOAEL ratio summary statistics finds (1) that such dependencies are complex and produce pronounced systematic errors and (2) that sampling error associated with typical sample sizes (50 chemicals) is non-negligible. These uncertainties strongly suggest that NOAEL ratio summary statistics cannot be taken at face value; conclusions based on such ratios reported in well over a dozen published papers should be reconsidered.     

转轨时期中国工业的效率与生产率——动态非参数生产前沿面理论及其应用

本文采用非参数生产率指数方法对转轨时期中国工业经济增长方式的转变程度进行定量研究。文中采用分区域的工业统计数据生产资源配置效率的变化和技术水平的变化两个方面测算了工业生产率的演化,并对生产率增长的内涵、经济增长方式转变的判据、区域性工业经济增长的特征等问题阐述了作者的观点。