one-way analysis of covariance is a popular and common statisticalmethod, wherein the equality of the means of several random variables whichhave a linear relationship with a random mathematical variable, is tested. inthis study, a method is presented to improve the one-way analysis of covari-ance when there is an uncertainty in accepting the statistical hypotheses. themethod deals with a fuzzy...

In this paper, we re-visit the inference problem for interval identi…ed parameters originally studied in Imbens and Manski (2004) and later extended in Stoye (2007). We establish a new con…dence interval that is asymptotically valid under the same assumptions as in Stoye (2007). Like the con…dence interval of Stoye (2007), our new con…dence interval extends that of Imbens and Manski (2004) to a...

—Experts are often not 100% con dent in their statements. In traditional fuzzy logic, the expert's degree of con dence in each of his or her statements is described by a number from the interval [0, 1]. However, due to similar uncertainty, an expert often cannot describe his or her degree by a single number. It is therefore reasonable to describe this degree by, e.g., a set of numbers. In this ...

One-sided con&dence intervals in the binomial, negative binomial, and Poisson distributions are considered. It is shown that the standard Wald interval su4ers from a serious systematic bias in the coverage and so does the one-sided score interval. Alternative con&dence intervals with better performance are considered. The coverage and length properties of the con&dence intervals are compared th...

So far, we have been considering point estimation. In this lecture, we will study interval estimation. Let X denote our data. Let θ ∈ R be our parameter of interest. Our task is to construct a data-dependent interval [l(X), r(X)] so that it contains θ with large probability. One possibility is to set l(X) = −∞ and r(X) = +∞. Such an interval will contain θ with probabity 1. Of course, the probl...

The general properties of two commonly used methods of interval estimation for population parameters in physics are examined Both of these methods em ploy the likelihood function i Obtaining an interval by nding the points where the likelihood decreases from its maximum by some speci ed ratio ii Obtaining an interval by nding points corresponding to some speci ed fraction of the total integral ...

Distributed Shared Memory(DSM) has gained popular acceptance by combining the scalability and low cost of distributed system with the ease of use of single address space. Many new hardware DSM and software DSM systems were proposed in recent years. In general, benchmarking is widely used to demonstrate the performance advantages of new systems. However, the common method used to summarize the m...

For binary matched-pairs data, this article discusses interval estimation of the di erence of probabilities 7 and an odds ratio for comparing ‘success’ probabilities. We present simple improvements of the commonly used Wald con dence intervals for these parameters. The improvement of the interval for the 9 di erence of probabilities is to add two observations to each sample before applying it. ...

This paper studies performance of both point and interval predictors of technical ine ciency in the stochastic production frontier model using a Monte Carlo experiment. In point prediction we use the Jondrow et al. (1980) results, while for interval prediction the Horrace and Schmidt (1996) and Hjalmarsson et al. (1996) results are used. When ML estimators are used we nd negative bias in point ...

An important objective in the evaluation of algorithms with sensory inputs is the devel opment of measures characterizing the intrinsic errors in the results Intrinsic are those errors which are caused by noise in the input data The particular application which we consider is D reconstruction from stereo We demonstrate that a radiometric correction of the images could improve signi cantly the a...