Comments on the primary and scatter dose-spread kernels used for convolution methods

The basic primary and scatter dose-spread kernels used for convolution methods are usually produced by Monte Carlo simulations with the interaction point forced to the center of a large water phantom. However, it is still not clear whether such Monte Carlo based kernels allow accurate dose calculations with a wide range of field sizes and depths, especially in thorax phantoms. Using the differential primary and scatter concept, this paper proposes another type of basic kernel, with which perfectly accurate primary and scatter absorbed dose calculations can be performed under conditions that the beam is parallel, the incident beam intensity is uniform within and zero outside the field, and the primary beam attenuation coefficient along raylines is not a function of depth and off-axis distance.     

排序集抽样下Inverse Rayleigh分布的Fisher信息量及其在参数估计中的应用

文章分别在简单随机抽样和排序集抽样下研究了Inverse Rayleigh分布中对应样本所含刻度参数θ的Fisher信息量.数值结果表示,同等样本容量的排序集样本比简单随机样本提供更多关于θ的信息.接着分别基于简单随机样本和排序集样本构造了θ的一些优良估计,并对估计结果进行了数值比较.     

Estimation of a quantile in some nonstandard cases

A necessary condition for the asymptotic normality of the sample quantile estimator isf(Q(p))=F(Q(p))>0, whereQ(p) is thep-th quantile of the distribution functionF(x). In this paper, we estimate a quantile by a kernel quantile estimator when this condition is violated. We have shown that the kernel quantile estimator is asymptotically normal in some nonstandard cases. The optimal convergence rate of the mean squared error for the kernel estimator is obtained with respect to the asymptotically optimal bandwidth. A law of the iterated logarithm is also established.This research was partially supported by the new faculty award from the University of Oregon.     

Sequential estimation of a parameter of an exponential distribution

We consider the problem of minimum risk point estimation for the parameter =a+b of the exponential distribution with unknown location parameter and scale parameter when the loss function is squared error plus linear cost. In this paper, we propose a sequential estimator of and show that the associated risk is asymptotically one cost less than that given by Ghosh and Mukhopadhyay (1989,South African Statist. J.,23, 251–268).     

Mass shifting roles of negative kernel mass in density estimation

f(x) is a univariate density in C ⁴ with bounded support. For any n and sufficiently small kernel bandwidths, the symmetric appendage of any negative mass, –U, to any smooth unimodal symmetric kernel of order p=2 shifts expected estimator mass from regions where f(x)>0 to regions where f(x)<0. For large n, the mean automatic kernel adaptation induced by –U is analyzed in the simplest MISE reduction scenario: The symmetric appendage of –U to the uniform kernel K(x, X) over MISE-optimal bandwidths reduces MISE by shifting K(x, X) mass asymmetrically across the observation X in the direction of decreasing |f(x)|.     

A central limit theorem for age- and density-dependent population processes

Consider a population consisting of one type of individual living in a fixed region with area A. In [8], we constructed a stochastic population model in which the death rate is affected by the age of the individual and the birth rate is affected by the population density P_A(t), i.e., the population size divided by the area A of the given region. In [8], we proposed a continuous deterministic model which in general is a nonlinear Volterra type integral equation and proved that under appropriate conditions the sequence P_A(t) would converge to the solution P(t) of our integral equation in the sense that

$\text{lim}\text{→∞}\text{P}\text{sup}\text{0?s?t}\text{|P}{}_{\mathrm{A}}\text{(s) ? P(s)|>ε}\text{=0}\text{for every}\text{ε > 0}$

.In this paper, we obtain a “central limit theorem” for the random element √A(P_A(t)?P(t)). We prove that under appropriate conditions √A(P_A(t)?P(t)) will converge to a Gaussian process. (See Theorem 3.4 for the explicit formula of this Gaussian process.)     

Evaluation of the use of consensus values in proficiency testing programmes

Proficiency testing (PT) is an essential tool used by laboratory accreditation bodies to assess the competency of laboratories. Because of limited resources of PT providers or for other reasons, the assigned reference value used in the calculation of z-score values has usually been derived from some sort of consensus value obtained by central tendency estimators such as the arithmetic mean or robust mean. However, if the assigned reference value deviates significantly from the ‘true value’ of the analyte in the test material, laboratories’ performance will be evaluated incorrectly. This paper evaluates the use of consensus values in proficiency testing programmes using the Monte Carlo simulation technique. The results indicated that the deviation of the assigned value from the true value could be as large as 40%, depending on the parameters of the proficiency testing programmes under investigation such as sample homogeneity, number of participant laboratories, concentration level, method precision and laboratory bias. To study how these parameters affect the degree of discrepancy between the consensus value and the true value, a fractional factorial design was also applied. The findings indicate that the number of participating laboratories and the distribution of laboratory bias were the prime two factors affecting the deviation of the consensus value from the true value.     

Fatty Acid Composition of Pistachio Nuts in Turkey

In this study oil yields and fatty acid compositions in the kernels of two varieties (Uzun and Siirt) of pistachio grown in different region of Turkey were investigated. Kernel oils were obtained by Soxhlet extraction using petroleum ether. The yields were found to be 57.1-58.9% and 56.1-62.6 respectively for the Uzun and Siirt varieties, on a moisture-free basis. Fatty acid composition of oils were analysed by GC/MS in the methyl ester form. Fourteen fatty acid components representing about 99% of the total oils were characterised. Oleic acid (55.4-62.6% and 60.7-65.5%, respectively) was the main fatty acid component in both varieties. Pentadecanoic acid, (Z)-7-hexadecenoic acid, margaric acid, Z-7-octadecenoic acid, arachidic acid, 11-eicosenoic acid, and behenic acid were detected in both varieties for the first time.     

Durbin-Watson statistic as a morphological estimator of information content

The present work proposes a new approach for the evaluation of the information content in latent variables, and therefore, for the determination of the regression model dimensionality. Several examples are provided, using simulated, real-world, and reference datasets. The results showed that the application of the Durbin-Watson (DW) criterion could be used for the determination of the number of latent variables. Moreover, the method is straightforward in its implementation and could help in the understanding of model behaviour, particularly in complex datasets. A comparison is made with cross-validation techniques for the case of reference datasets, showing the potential of the Durbin-Watson criterion in the characterisation of the regression model. The advantages and disadvantages of this procedure (compared to cross-validation) are discussed. The properties of the information content of the regression vectors (loadings p, w and b vectors) are shown as well as how to use them for the current purpose.     

一般增长曲线模型参数阵的BLU估计

考虑一般增长曲线模型：Ｙ＝Ｘ１ＢＸ２＋εＥ（Ｖｅｃ（ε））＝０Ｖ（Ｖｅｃ（ε））＝σ２ＶＩｎ（Ｖ０）本文对任一可估函数ＫＢＬ给出了它的ＢＬＵ估计（最佳线性无偏估计），并得到了方差σ２的一个无偏估计．