福建省不同景观资源分布特征的比较分析

采用最近邻点距离法,对福建省省级以上风景名胜区、自然保护区及森林公园空间分布特征进行研究,同时对3种景观资源的景源现状及特征进行了比较。研究表明,福建省总体景观资源空间分布为显著的聚集分布,其中风景名胜区、森林公园及自然保护区等3种景观资源均为随机分布,3种景观资源的景源特征差异比较大,建议在景观较稀疏的区域增加新的景区,通过各种廊道连接构成全省景观网络系统,以保证全省景观资源系统的健康有序发展。     

A comparative study of smoothing procedures for ordered categorical data

Methods for estimating probabilities on sample spaces for ordered-categorical variables are surveyed. The methods all involve smoothing the relative frequencies in manners which recognise the ordering among categories. Approaches of this type include convex smoothing, weighting-function and kernel-based methods, near neighbour methods, Bayes-based methods and penalized minimum-distance methods. The relationships among the methods are brought out, application is made to a medical example and a simulation study is reported which compares the methods on univariate and bivariate examples. Links with smoothing procedures in other contexts are indicated.     

VALID RANDOMISATION FOR THE FIRST-DIFFERENCE ANALYSIS

A general method is presented for randomising a block design while preserving the neighbour relationships between treatments. The randomisation possesses validity properties for the first-difference analysis introduced by Besag & Kempton (1986). The estimators of treatment differences are unbiased, and the paper shows how to calculate quadratic estimators of their variance, which are unbiased if treatment effects and plot effects are additive. Simplifications, which appear when the design is neighbour-balanced, are described and illustrated.     

A fully nonparametric diagnostic test for homogeneity of variances

The authors propose a new nonparametric diagnostic test for checking the constancy of the conditional variance function σ²(x) in the regression model Y_i = m(x_i) + σ(x_i)?_i, i = 1,…, m. Their test, which does not assume a known parametric form for the conditional mean function m(x), is inspired by a recent asymptotic theory in the analysis of variance when the number of factor levels is large. The authors demonstrate through simulations the good finite‐sample properties of the test and illustrate its use in a study on the effect of drug utilization on health care costs.     

基于K-近邻方法的网络信息文本分类

提出了一种网络信息文本分类模型的建立方法，根据网络报文的特点，抽取其中关键词作为分类特征词条，并以报文关键词进行词频统计分析建立文本分模型，分别进行了基于最近邻决策和K-近邻决策的分类效果试验研究，结果显示，K-近邻决策的分类效果要优于最近邻决策的分类效果。     

Automatic breakpoint detection for retrospective cumulative sum charts

Cumulative sum (cusum) methods can be used for monitoring processes and for retrospective (historical) data analysis. Most software only provides the former. The comment by Williamson that retrospective cusum analysis is a neglected area is still true. Though not in vogue, retrospective cusum analysis is useful for investigations such as benchmarking of processes, identifying causes of process decay, selecting reference data sets for typicality studies, and reporting of historical data. Even those texts which cover retrospective analyses, usually ignore the question of identifying multiple points of change (breakpoints), and present essentially manual methods for assessing single breakpoints. Most users of statistical methods want software solutions that are easy to use and require little user intervention or interpretation. Direct implementation of manual method does not give a user robust solution. Problems are illustrated. Attempts to use monitoring CuSums in retrospective analysis can also lead to errors. A practical recursive method is presented for breakpoint identification and significance assessment, which can be automated. Copyright © 2002 John Wiley & Sons, Ltd.     

Prediction of Chaotic Time Series in the Presence of Measurement Error: the Importance of Initial Conditions

In this paper we argue that even if a dynamic relationship can be well described by a deterministic system, retrieving this relationship from an empirical time series has to take into account some, although possibly very small measurement error in the observations. Therefore, measuring the initial conditions for prediction may become much more difficult since one now has a combination of deterministic and stochastic elements. We introduce a partial smoothing estimator for estimating the unobserved initial conditions. We will show that this estimator allows to reduce the effects of measurement error for predictions although the reduction may be small in the presence of strong chaotic dynamics. This will be illustrated using the logistic map.     

Optimal Circular Block Designs for Neighbouring Competition Effects

Competition or interference occurs when the responses to treatments in experimental units are affected by the treatments in neighbouring units. This may contribute to variability in experimental results and lead to substantial losses in efficiency. The study of a competing situation needs designs in which the competing units appear in a predetermined pattern. This paper deals with optimality aspects of circular block designs for studying the competition among treatments applied to neighbouring experimental units. The model considered is a four-way classified model consisting of direct effect of the treatment applied to a particular plot, the effect of those treatments applied to the immediate left and right neighbouring units and the block effect. Conditions have been obtained for the block design to be universally optimal for estimating direct and neighbour effects. Some classes of balanced and strongly balanced complete block designs have been identified to be universally optimal for the estimation of direct, left and right neighbour effects and a list of universally optimal designs for v<20 and r<100 has been prepared.     

Nonparametric regression: An up–to–date bibliography

We attempt to give a complete list of references in non parametric regression estimation (including non parametric time series analysis), with a brief introduction of these works according a classification taking the diversity of problems or methods into account.     

A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects

In this paper, we present a test of independence between the response variable, which can be discrete or continuous, and a continuous covariate after adjusting for heteroscedastic treatment effects. The method involves first augmenting each pair of the data for all treatments with a fixed number of nearest neighbours as pseudo‐replicates. Then a test statistic is constructed by taking the difference of two quadratic forms. The statistic is equivalent to the average lagged correlations between the response and nearest neighbour local estimates of the conditional mean of response given the covariate for each treatment group. This approach effectively eliminates the need to estimate the nonlinear regression function. The asymptotic distribution of the proposed test statistic is obtained under the null and local alternatives. Although using a fixed number of nearest neighbours pose significant difficulty in the inference compared to that allowing the number of nearest neighbours to go to infinity, the parametric standardizing rate for our test statistics is obtained. Numerical studies show that the new test procedure has robust power to detect nonlinear dependency in the presence of outliers that might result from highly skewed distributions. The Canadian Journal of Statistics 38: 408–433; 2010 © 2010 Statistical Society of Canada