Kernel density estimation with binned data

Continuous data are often measured or used in binned or rounded form. In this paper we follow up on Hall's work analyzing the effect of using equally-spaced binned data in a kernel density estimator. It is shown that a surprisingly large amount of binning does not adversely affect the integrated mean squared error of a kernel estimate.     

Efficiency and robustness of hogg’s adaptive trimmed means

The adaptive trimmed means of Hogg (1974) and modified by De Wet and van Wvk (1978) are studied in this paper for finite samples. They are shown to have good intervals and afficiency Properties for Sample sizes 20 and larger Confidence intervals pased on these estimator are also considered and found to be fairly ro-bust for sample sizes 40 and larger.     

Takacs–Fiksel Method for Stationary Marked Gibbs Point Processes

Abstract. This article studies a method to estimate the parameters governing the distribution of a stationary marked Gibbs point process. This procedure, known as the Takacs–Fiksel method, is based on the estimation of the left and right hand sides of the Georgii–Nguyen–Zessin formula and leads to a family of estimators due to the possible choices of test functions. We propose several examples illustrating the interest and flexibility of this procedure. We also provide sufficient conditions based on the model and the test functions to derive asymptotic properties (consistency and asymptotic normality) of the resulting estimator. The different assumptions are discussed for exponential family models and for a large class of test functions. A short simulation study is proposed to assess the correctness of the methodology and the asymptotic results.     

Empirical assessment of the applicability of shrinkage predictors in regression when past and future data differ

Jones and Copas (1986) present theoretical and simulation results on the relative merits of a Stein predictor (Copas, 1983) and the ordinary least squares predictor in the usual linear multiple regression model, when certain distributional properties of the regressor variables arising in the past differ from those for which predictions are to be made. Here, extension is made to the practical situation where the true regression parameters are unknown. A hypothesis testing procedure is developed to help determine which of shrinkage and least squares is preferable in any given instance. This approach is applied to explain some empirical evidence on the comparative merits of the two procedures, recently given by Berk (1984).     

Robust tests for equality of two population means under the normal model

A robust procedure is developed for testing the equality of means in the two sample normal model. This is based on the weighted likelihood estimators of Basu et al. (1993). When the normal model is true the tests proposed have the same asymptotic power as the two sample Student's t-statistic in the equal variance case. However, when the normality assumptions are only approximately true the proposed tests can be substantially more powerful than the classical tests. In a Monte Carlo study for the equal variance case under various outlier models the proposed test using Hellinger distance based weighted likelihood estimator compared favorably with the classical test as well as the robust test proposed by Tiku (1980).     

Numerical algorithms for solving nonlinear L р-norm estimation problems: part II - a mixture method for large residual and illo-conditioned problems

The nonlinear least squares algorithm of Gill and Murray (1978) is extended and modified to solve nonlinear L _р-norm estimation problems efficiently. The new algorithm uses a mixture of 1st-order derivative (Guass-Newton) and 2nd-order derivative (Newton) search directions. A new rule for selecting the “grade” r of the p-jacobiab matrix J_p was also incorporated. This brought about rapid convergence of the algorithm on previously reported test examples.     

Inference procedures for the variance gamma model and applications

Goodness-of-fit tests for the family of the four-parameter normal–variance gamma distribution are constructed. The tests are based on a weighted integral incorporating the empirical characteristic function of suitably standardized data. Non-standard algorithms are employed for the computation of the maximum-likelihood estimators of the parameters involved in the test statistic, while Monte Carlo results are used in order to compare the new test with some classical goodness-of-fit methods. A real-data application is also included.     

A control in stratified sampling

A new expression of the variance of the units from a stratified finite population wit L strata, is obtained in funcions of the means L–1 strata, and in conseqeuence we obtained formulae which relates the mean of a stratum in a statistical study with the population mean and the ones of the remaining strate. As an application, we obtained a useful checking of the estimation consistency by stratified sampling in any precies survey     

Markov Random Fields with Higher-order Interactions

Discrete-state Markov random fields on regular arrays have played a significant role in spatial statistics and image analysis. For example, they are used to represent objects against background in computer vision and pixel-based classification of a region into different crop types in remote sensing. Convenience has generally favoured formulations that involve only pairwise interactions. Such models are in themselves unrealistic and, although they often perform surprisingly well in tasks such as the restoration of degraded images, they are unsatisfactory for many other purposes. In this paper, we consider particular forms of Markov random fields that involve higher-order interactions and therefore are better able to represent the large-scale properties of typical spatial scenes. Interpretations of the parameters are given and realizations from a variety of models are produced via Markov chain Monte Carlo. Potential applications are illustrated in two examples. The first concerns Bayesian image analysis and confirms that pairwise-interaction priors may perform very poorly for image functionals such as number of objects, even when restoration apparently works well. The second example describes a model for a geological dataset and obtains maximum-likelihood parameter estimates using Markov chain Monte Carlo. Despite the complexity of the formulation, realizations of the estimated model suggest that the representation is quite realistic.     

相干分布式目标一维波达方向搜索迭代估计方法

在信号源角分布函数具体的数学形式未知的情况下,基于角分布函数共轭对称性约束条件,提出了一种相干分布式目标一维波达方向搜索迭代估计方法,该方法不用多维参数搜索,并适用于角信号分布函数形式不同的分布式目标同时存在的情况。