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By
Sansó, B.
Summary
Consider a Normal likelihood with unknown mean and variance and consider a prior given by the product of a Studentt for the mean and a noninformative prior for the variance. The resulting posterior for the mean is proportional to the product of two Studentt densities. Approximations are given for the posterior moments of such a density by recalling the fact that the Studentt is a scale mixture of Normals and performing appropriate Taylor expansions. Extensions in can be obtained for a oneway random effects model and its applications in metaanalysis.
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By
Olsen, Jørn; Basso, Olga
10 Citations
In writing this chapter we assumed that the reader is familiar with the basic concepts in epidemiology. You will not find any overview of different designs, measure of disease occurrence, standardisations, other ways of adjusting for confounders, or any general discussion on bias, confounding or on measuring effects. If you are not familiar with these topics you should start by reading other parts of the book or turn to one of the many fine available textbooks.
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By
Soni, Pooja; Dewan, Isha; Jain, Kanchan
In this paper, nonparametric procedures for testing equality of quantiles against an ordered alternative are proposed. These testing procedures are based on two different estimators of the quantile function available in literature. Limiting distributions of the test statistics are derived. Simulations have been carried out to check the performance of the tests.
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By
Lehmann, E. L.; Lehmann, E. L.
This is an account of the life of the author’s book Testing Statistical Hypotheses, its genesis, philosophy, reception and publishing history. There is also some discussion of the position of hypothesis testing and the NeymanPearson theory in the wider context of statistical methodology and theory.
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By
Lu, Tao; Wang, Shuli; Li, Jingshan; Lucas, Paul; Anderson, Mike; Ross, Kay
Show all (6)
4 Citations
In this paper, we introduce a simulation study to improve the antineoplastic medication preparation and delivery performance at a pharmacy department in a large community hospital. The goal of this work is to help pharmacy reduce patients’ average waiting time when receiving chemotherapy. This will be achieved by simulating and analyzing the preparation and delivery procedures to identify process bottlenecks, carry out whatif analysis, predict the impact of improvement effort, and provide recommendations to hospital leadership. Using the simulation model, we discover that by introducing early preparation for the returning patients and dedicating an infusion staff member for medication delivery, patients’ waiting time for antineoplastic medications can be reduced substantially. Such improvements do not require additional floor space or significant investment. The recommendation has been accepted by hospital management and implemented in the pharmacy department. The preliminary results have verified the simulation output with the desired improvement predicted by the model.
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By
Shinmura, Shuichi
Chapter
1
explained the new theory of discriminant analysis after R. A. Fisher (Theory). The theory solved five problems completely. Especially, Revised IPOLDF (RIP) and Method2 firstly succeeded in the cancer gene analysis. RIP could find six microarrays were LSD (Fact3). LINGO Program3 of Method2 could decompose the microarray into many SMs and another noise subspace (Fact4). In Chap.
2
, we make signal data made by RIP discriminant scores (RipDSs). Our breakthrough opens the new frontier of cancer gene diagnosis and malignancy indexes. We find the new problem (Problem6): “Why could no researchers find the linear separable facts in microarrays and SM from 1970?” In this book, we explain the several answers of Problem6. In this chapter, we survey how to make different RipDSs from many SMs. It explains why microarray consists of many SMs and the different RipDSs. By these results, we wish to classify SMs into several categories of malignancy indexes in the future.
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By
Lee, Alan; Willcox, Bobby
10 Citations
In this paper, we consider several generalizations of the popular Ward’s method for agglomerative hierarchical clustering. Our work was motivated by clustering software, such as the R function hclust, which accepts a distance matrix as input and applies Ward’s definition of intercluster distance to produce a clustering. The standard version of Ward’s method uses squared Euclidean distance to form the distance matrix. We explore the effect on the clustering of using other definitions of distance, such as the Minkowski distance.
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