Mean squared prediction error in the spatial linear model with estimated covariance parameters

The problem considered is that of predicting the value of a linear functional of a random field when the parameter vector of the covariance function (or generalized covariance function) is unknown. The customary predictor when is unknown, which we call the EBLUP, is obtained by substituting an estimator j for in the expression for the best linear unbiased predictor (BLUP). Similarly, the customary estimator of the mean squared prediction error (MSPE) of the EBLUP is obtained by substituting j for in the expression f for the BLUP's MSPE; we call this the EMSPE. In this article, the appropriateness of the EMSPE as an estimator of the EBLUP's MSPE is examined, and alternative estimators of the EBLUP's MSPE for use when the EMSPE is inappropriate are suggested. Several illustrative examples show that the performance of the EMSPE depends on the strength of spatial correlation; the EMSPE is at its best when the spatial correlation is strong.This research was partially supported by a University of Iowa Old Gold Fellowship (Zimmerman) and by the NSF under grant DMS-8703083 (Cressie).     

Neutral losses: A type of important variables in prediction of branching degree for acyclic alkenes from mass spectra

Neutral losses are a type of important variables in mass spectral interpretation. Since it is hard to calculate or extract neutral losses from mass spectra, they are usually discarded. In this study, dissimilarity analysis was employed to extract mass spectral characteristics for predicting branching degree of acyclic alkenes. The relationships between branching degree and neutral loss were constructed under direction of experimental observation and mass spectral fragmentations. A branching degree predictor of acyclic alkenes was subsequently built based on the above relationships. After tested by the experimental data in previous studies, the predictor could correctly provide the branching degree from abundant ions of mass spectra. More importantly, this predictor was able to point out which acyclic alkenes could be predicted correctly or not.     

泊松分布中一个参数的两个最大似然估计的比较

给出泊松分布中参数e-λ的两个最大似然估计,讨论了不同标准条件下各自的性质,指出在大样本条件下,两者具有相当的性质.     

Discrete phase-space structure of n-qubit mutually unbiased bases

We work out the phase-space structure for a system of n qubits. We replace the field of real numbers that label the axes of the continuous phase space by the finite field GF(2ⁿ) and investigate the geometrical structures compatible with the notion of unbiasedness. These consist of bundles of discrete curves intersecting only at the origin and satisfying certain additional properties. We provide a simple classification of such curves and study in detail the four- and eight-dimensional cases, analyzing also the effect of local transformations. In this way, we provide a comprehensive phase-space approach to the construction of mutually unbiased bases for n qubits.     

Bad Luck In Quadratic Improvement of The Linear Estimator In A Special Linear Model

The paper concludes our investigations in looking for the locally best linear-quadratic estimators of mean value parameters and of the covariance matrix elements in a special structure of the linear model (2 variables case) where the dispersions of the observed quantities depend on the mean value parameters. Unfortunately there exists no linear-quadratic improvement of the linear estimator of mean value parameters in this model.     

广义增长曲线模型参数阵的最佳线性无偏估计

对于增长曲线模型Y=X1BX′2+ε,E(ε)=O,D(ε)=D(Vecε)=V2V1,在V1,V2均为非负定矩阵的情况下,利用最小二乘统一理论及矩阵拉直方法,给出了未知参数阵B的可估矩阵函数Z1BZ′2的最佳线性无偏估计,将有关文献的结果推广到更一般的情况。     

A class of estimators with estimated optimum values in sample surveys

Srivastava and Jhajj (1981) proposed a class of estimators for population mean of a character using auxiliary information and optimum values involving unknown parameters. From the practical point of view, their results have very little utility. In view of practical utility, we propose a class of estimators with estimated optimum values. Further, it is shown that the proposed class with estimated optimum values attains the same minimum mean square error of the class of estimators based on optimum values.     

用monte Carlo法求积分区域为 曲面体的高维积分

本文讨论了用Monte Carlo法求积分区域为曲面体的高维积分，分别根据积分区域G的体积为已知和未知两种情形，给出两种估计法及收敛性定理.另外也对求曲面体的重心进行了讨论.     

Mutually unbiased bases

After a brief review of the notion of a full set of mutually unbiased bases in an N-dimensional Hilbert space, we summarize the work of Wootters and Fields (W K Wootters and B C Fields, Ann. Phys. 191, 363 (1989)) which gives an explicit construction for such bases for the case N=p ^r , where p is a prime. Further, we show how, by exploiting certain freedom in the Wootters-Fields construction, the task of explicitly writing down such bases can be simplified for the case when p is an odd prime. In particular, we express the results entirely in terms of the character vectors of the cyclic group G of order p. We also analyse the connection between mutually unbiased bases and the representations of G.