Combining Multivariate Bioassays: Accurate Inference Using Small Sample Asymptotics

For several independent multivariate bioassays performed at different laboratories or locations, the problem of testing the homogeneity of the relative potencies is addressed, assuming the usual slope‐ratio or parallel line assay model. When the homogeneity hypothesis holds, interval estimation of the common relative potency is also addressed. These problems have been investigated in the literature using likelihood‐based methods, under the assumption of a common covariance matrix across the different studies. This assumption is relaxed in this investigation. Numerical results show that the usual likelihood‐based procedures are inaccurate for both of the above problems, in terms of providing inflated type I error probabilities for the homogeneity test, and providing coverage probabilities below the nominal level for the interval estimation of the common relative potency, unless the sample sizes are large, as expected. Correction based on small sample asymptotics is investigated in this article, and this provides significantly more accurate results in the small sample scenario. The results are also illustrated with examples.     

在VB5.0中实现和单片机串行通信方法

本文具体介绍了利用ＶＢ５．０的通讯控件ＭＳＣＯＭＭ３２．ＯＣＸ和单片机进行串行通信的方法——查表法发达和接收。     

论纪录片叙事情节的连接方式

在纪录片叙事中,一个故事必须有一个序列,但更多的时候,一个故事往往由多个序列构成.如何安排这些序列,使它们之间的连接具有可续性的依据,从而构成情节.本文认为,序列与序列之间的连接可以有以下几种方式,即时间连接、空间连接、因果连接,此外,还应该有联想式连接等.这些情节连接方式的揭示,对纪录片的创作与欣赏具有一定的意义.     

Setup cost reduction a multi-stage order quantity model

In this paper, a single item, multi-stage serial order quantity (MSOQ) model with constant demand is discussed. The objective of the model is to minimize the total cost which includes the setup cost and the inventory holding cost. This paper examines and analyses the investment in a one-time cost to reduce the (current) setup level and adds a per unit item amortization of this cost to the other costs associated with the MSOQ model. We consider the setup cost to be decreased on each stage with the same rate and the cost of the joint setup cost reduction is a logarithmic cost function.     

The maximum likelihood estimators in the growth curve model with serial covariance structure

The maximum likelihood estimators of unknown parameters in the growth curve model with serial covariance structure under some conditions are derived in the paper.     

Heteroscedasticity-robust estimation of autocorrelation

This paper proposes estimators of the first-order autocorrelation that are based on suitably transformed ratios of successive observations. The new estimators are given by simple functions of the observations. Numerical optimization is not required. Simulations show that they are highly robust against extreme values and clusters of high volatility and are therefore particularly useful for the estimation of serial correlation in return series. Besides, the results of the simulation study also call into question the common practice of correcting the small-sample bias of conventional estimators.     

Modelling and analysis of a multi-stage order quantity model

A single-item, multi-stage serial order quantity MSOQ model with constant demand is discussed. An algorithm to determine the relationship between the lot sizes of the MSOQ model is also developed. Illustrative examples are presented.     

著名商标保护的屏障──商标“反淡化”理论的探索

商标“反淡化”理论是商标侵权理论之一。在我国虽然存在着不少商标淡化的事例，但却没有相关的“反淡化”立法，对此理论的研究也相对滞后。本文在借鉴国外“反淡化”立法和理论研究的基础上，对商标淡化的概念、种类及在我国进行“反淡化”立法的必要性作了初步探索。     

The Shifting Sands of Open Access Publishing,a Publisher's View

Abstract

This paper sets Open Access (OA) publishing in the context of today's scientific, technical, and medical (STM) publishing trends. Four areas are covered: (a) a brief overview of STM publishing and its value today; (b) OA's place in the industry; (c) the underlying economics of OA, particularly its author-pays model; and (d) directions in moving towards “universal access” to STM information, where both researchers and the public have access to the scientific information they need.     

Hidden Markov chains in generalized linear models

We show how the concept of hidden Markov model may be accommodated in a setting involving multiple sequences of observations. The resulting class of models allows for both interrelationships between different sequences and serial dependence within sequences. Missing values in the observation sequences may be handled in a straightforward manner. We also examine a group of methods, based upon the observed Fisher Information matrix, for estimating the covariance matrix of the parameter estimates. We illustrate the methods with both real and simulated data sets.