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## Publishing Your Results

### Permutation Tests (2000-01-01): 179-184 , January 01, 2000

McKinney et al [1989] report that more than half the published articles that apply Fisher’s exact test do so improperly. Our own survey of some 50 biological and medical journals supports their findings. This chapter provides you with a positive prescription for the successful application and publication of the results of resampling procedures. First, we consider the rules you must follow to ensure that your data can be analyzed by statistical and permutation methods. Then, we describe two commercially available computer programs that can perform a wide variety of permutation analyses. Finally, we provide you with five simple rules to prepare your report for publication.

## Simultan stärkste unverfälschte Tests

### Metrika (1961-12-01) 4: 158-168 , December 01, 1961

## Homogene und heterogene Teilnahmeeffekte des Hamburger Kombilohnmodells: Ein Verfahrensvergleich von Propensity Score Matching und linearer Regression

### AStA Wirtschafts- und Sozialstatistisches Archiv (2009-06-01) 3: 41-65 , June 01, 2009

### Zusammenfassung

Der vorliegende Beitrag untersucht die Teilnahmeeffekte des Hamburger Kombilohnmodells mit Daten der Stichprobe der Integrierten Erwerbsbiografien. Hierfür werden neben dem Verfahren des Propensity Score Matching auch lineare Regressionen durchgeführt. Die so ermittelten Teilnahmeeffekte variieren nur geringfügig zwischen den beiden Evaluierungsmethoden. Jedoch ist die Ermittlung und Interpretation von heterogenen Teilnahmeeffekten mit linearen Regressionen einfacher als mit Matching Methoden. Insgesamt zeigen sich stark positive Teilnahmeeffekte, die bei Problemgruppen am Arbeitsmarkt höher ausfallen. Daher scheint das Hamburger Kombilohnmodell neben allgemein positiven Wirkungen auch zielgruppenorientiert zu sein.

## Application I: Spread of Influenza

### Inference for Diffusion Processes (2013-01-01): 281-303 , January 01, 2013

As a first application of the methods introduced in the first two parts of this book, this chapter investigates the spread of human influenza. More precisely, it analyses a well-known dataset on an influenza outbreak in a British boarding school and the spatial spread of influenza in Germany during the season 2009/10, in which the swine flu virus was prevalent. In the latter example, spatial mixing of individuals is estimated from commuter data. Modelling is based on diffusion approximations derived in Chap. 5 . Statistical inference is carried out using a Bayesian approach developed in Chap. 7 .

## Asymptotic behaviour and statistical applications of divergence measures in multinomial populations: a unified study

### Statistical Papers (1995-12-01) 36: 1-29 , December 01, 1995

Divergence measures play an important role in statistical theory, especially in large sample theories of estimation and testing. The underlying reason is that they are indices of statistical distance between probability distributions P and Q; the smaller these indices are the harder it is to discriminate between P and Q. Many divergence measures have been proposed since the publication of the paper of Kullback and Leibler (1951). Renyi (1961) gave the first generalization of Kullback-Leibler divergence, Jeffreys (1946) defined the J-divergences, Burbea and Rao (1982) introduced the R-divergences, Sharma and Mittal (1977) the (r,s)-divergences, Csiszar (1967) the ϕ-divergences, Taneja (1989) the generalized J-divergences and the generalized R-divergences and so on. In order to do a unified study of their statistical properties, here we propose a generalized divergence, called (*h*,*ϕ*)-divergence, which include as particular cases the above mentioned divergence measures. Under different assumptions, it is shown that the asymptotic distributions of the (*h*,*ϕ*)-divergence statistics are either normal or chi square. The chi square and the likelihood ratio test statistics are particular cases of the (*h*,*ϕ*)-divergence test statistics considered. From the previous results, asymptotic distributions of entropy statistics are derived too. Applications to testing statistical hypothesis in multinomial populations are given. The Pitman and Bahadur efficiencies of tests of goodness of fit and independence based on these statistics are obtained. To finish, apendices with the asymptotic variances of many well known divergence and entropy statistics are presented.

## Upper bound problem in linear fractional functionals programming

### Metrika (1970-12-01) 15: 81-85 , December 01, 1970

## Hierarchical Bayesian modeling in the environmental sciences

### Allgemeines Statistisches Archiv (2000-07-06) 84: 141-153 , July 06, 2000

### Summary:

The Bayesian statistical paradigm provides an valuable framework for dealing with the uncertainties present in the environmental sciences. In particular, the hierarchical approach is well-suited to formulate complex models for complex environmental phenomena. I discuss a simple, but useful hierarchical linear model tailored to analyze spatial data and inference problems. Ideas are illustrated in an example concerning the estimation of near-surface winds fields over the Labrador Sea. Next, a collection of examples demonstrating the power of hierarchical modeling are presented. These include combining datasets and a variety of space-time modeling approaches. Finally, notions and examples of how hierarchical Bayesian modeling provides a mechanism for developing large-scale analyses bridging different sciences are discussed.

## Production, metals and construction

### Monthly Digest of Statistics (2011-05-01) 785: 60-74 , May 01, 2011

## Detecting Differentially Expressed Genes with RNA-seq Data Using Backward Selection to Account for the Effects of Relevant Covariates

### Journal of Agricultural, Biological, and Environmental Statistics (2015-12-01) 20: 577-597 , December 01, 2015

A common challenge in analysis of transcriptomic data is to identify differentially expressed genes, i.e., genes whose mean transcript abundance levels differ across the levels of a factor of scientific interest. Transcript abundance levels can be measured simultaneously for thousands of genes in multiple biological samples using RNA sequencing (RNA-seq) technology. Part of the variation in RNA-seq measures of transcript abundance may be associated with variation in continuous and/or categorical covariates measured for each experimental unit or RNA sample. Ignoring relevant covariates or modeling the effects of irrelevant covariates can be detrimental to identifying differentially expressed genes. We propose a backward selection strategy for selecting a set of covariates whose effects are accounted for when searching for differentially expressed genes. We illustrate our approach through the analysis of an RNA-seq study intended to identify genes differentially expressed between two lines of pigs divergently selected for residual feed intake. We use simulation to show the advantages of our backward selection procedure over alternative strategies that either ignore or adjust for all measured covariates.

## A note on optimum spacing of observations from a continuous time simple Markov process

### Metrika (1986-12-01) 33: 217-222 , December 01, 1986

### Summary

Assume that*X*(τ) is a continuous time simple Markov process with a parameter θ. The problem is to choose observation points τ_{0} < τ_{1} <...<τ_{T} which provide with the maximum possible information on θ. Suppose that the observation points are equally spaced, that is, for*t*=1, ...,*T*, T, τ;_{t}−τ_{t−1} is constant. Then the optimum value for*s* is obtained.