The Experts below are selected from a list of 45936 Experts worldwide ranked by ideXlab platform
Roger J Lewis - One of the best experts on this subject based on the ideXlab platform.
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advanced statistics bootstrapping confidence intervals for statistics with difficult distributions
Academic Emergency Medicine, 2005Co-Authors: Jason S Haukoos, Roger J LewisAbstract:The use of confidence intervals in reporting results of research has increased dramatically and is now required or highly recommended by editors of many scientific journals. Many resources describe methods for computing confidence intervals for statistics with mathematically simple distributions. Computing confidence intervals for descriptive statistics with distributions that are difficult to represent mathematically is more challenging. The bootstrap is a computationally intensive Statistical technique that allows the researcher to make inferences from data without making strong distributional assumptions about the data or the statistic being calculated. This allows the researcher to estimate confidence intervals for statistics that do not have simple sampling distributions (e.g., the median). The purposes of this article are to describe the concept of bootstrapping, to demonstrate how to estimate confidence intervals for the median and the Spearman rank correlation coefficient for non-normally-distributed data from a recent clinical study using two commonly used Statistical software packages (SAS and Stata), and to discuss specific limitations of the bootstrap.
Jason S Haukoos - One of the best experts on this subject based on the ideXlab platform.
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advanced statistics bootstrapping confidence intervals for statistics with difficult distributions
Academic Emergency Medicine, 2005Co-Authors: Jason S Haukoos, Roger J LewisAbstract:The use of confidence intervals in reporting results of research has increased dramatically and is now required or highly recommended by editors of many scientific journals. Many resources describe methods for computing confidence intervals for statistics with mathematically simple distributions. Computing confidence intervals for descriptive statistics with distributions that are difficult to represent mathematically is more challenging. The bootstrap is a computationally intensive Statistical technique that allows the researcher to make inferences from data without making strong distributional assumptions about the data or the statistic being calculated. This allows the researcher to estimate confidence intervals for statistics that do not have simple sampling distributions (e.g., the median). The purposes of this article are to describe the concept of bootstrapping, to demonstrate how to estimate confidence intervals for the median and the Spearman rank correlation coefficient for non-normally-distributed data from a recent clinical study using two commonly used Statistical software packages (SAS and Stata), and to discuss specific limitations of the bootstrap.
Martin Kulldorff - One of the best experts on this subject based on the ideXlab platform.
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constrained spanning tree algorithms for irregularly shaped spatial clustering
Computational Statistics & Data Analysis, 2012Co-Authors: Marcelo Azevedo Costa, Renato M Assuncao, Martin KulldorffAbstract:Spatial clustering methodologies that are capable of detecting and delineating irregular clusters can provide information about the geographical spread of various diseases under surveillance. This paper proposes and compares three spatial scan statistics designed to detect clusters with irregular shapes. The proposed methods use geographical boundary information to construct a graph in which a cluster growing process is performed based on likelihood function maximization. Constraints on cluster shape are imposed through early stopping, a double connection requirement and a maximum linkage criteria. The methods are evaluated using simulated data sets with either circular or irregular clusters, and compared to the circular and elliptic scan statistics. Results show that for circular clusters, the standard circular scan statistic is optimal, as expected. The double connection, elliptic maximum linkage scan statistics also achieve good results. For irregularly-shaped clusters, the elliptic, maximum linkage and double connected scan statistics are optimal for different cluster models and by different evaluation criteria, but the circular scan statistic also performs well. If the emphasis is on Statistical power for cluster detection, the simple circular scan statistic is attractive across the board choice. If the emphasis is on the accurate determination of cluster size, shape and boundaries, the double connected, maximum linkage and elliptical scan statistics are often more suitable choices. All methods perform well though, with the exception of the unrestricted dynamic minimum spanning tree scan statistic and the early stopping scan statistic, which we do not recommend.
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a spatial scan statistic for ordinal data
Statistics in Medicine, 2007Co-Authors: Inkyung Jung, Martin Kulldorff, Ann C KlassenAbstract:Spatial scan statistics are widely used for count data to detect geographical disease clusters of high or low incidence, mortality or prevalence and to evaluate their Statistical significance. Some data are ordinal or continuous in nature, however, so that it is necessary to dichotomize the data to use a traditional scan statistic for count data. There is then a loss of information and the choice of cut-off point is often arbitrary. In this paper, we propose a spatial scan statistic for ordinal data, which allows us to analyse such data incorporating the ordinal structure without making any further assumptions. The test statistic is based on a likelihood ratio test and evaluated using Monte Carlo hypothesis testing. The proposed method is illustrated using prostate cancer grade and stage data from the Maryland Cancer Registry. The Statistical power, sensitivity and positive predicted value of the test are examined through a simulation study.
Ann C Klassen - One of the best experts on this subject based on the ideXlab platform.
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a spatial scan statistic for ordinal data
Statistics in Medicine, 2007Co-Authors: Inkyung Jung, Martin Kulldorff, Ann C KlassenAbstract:Spatial scan statistics are widely used for count data to detect geographical disease clusters of high or low incidence, mortality or prevalence and to evaluate their Statistical significance. Some data are ordinal or continuous in nature, however, so that it is necessary to dichotomize the data to use a traditional scan statistic for count data. There is then a loss of information and the choice of cut-off point is often arbitrary. In this paper, we propose a spatial scan statistic for ordinal data, which allows us to analyse such data incorporating the ordinal structure without making any further assumptions. The test statistic is based on a likelihood ratio test and evaluated using Monte Carlo hypothesis testing. The proposed method is illustrated using prostate cancer grade and stage data from the Maryland Cancer Registry. The Statistical power, sensitivity and positive predicted value of the test are examined through a simulation study.
Peter Dixon - One of the best experts on this subject based on the ideXlab platform.
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likelihood ratios a simple and flexible statistic for empirical psychologists
Psychonomic Bulletin & Review, 2004Co-Authors: Scott Glover, Peter DixonAbstract:Empirical studies in psychology typically employ null hypothesis significance testing to draw Statistical inferences. We propose that likelihood ratios are a more straightforward alternative to this approach. Likelihood ratios provide a measure of the fit of two competing models; the statistic represents a direct comparison of the relative likelihood of the data, given the best fit of the two models. Likelihood ratios offer an intuitive, easily interpretable statistic that allows the researcher great flexibility in framing empirical arguments. In support of this position, we report the results of a survey of empirical articles in psychology, in which the common uses of statistics by empirical psychologists is examined. From the results of this survey, we show that likelihood ratios are able to serve all the important Statistical needs of researchers in empirical psychology in a format that is more straightforward and easier to interpret than traditional inferential statistics.
