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On the Statistical Comparison of Inductive Learning Methods
 In D. Fisher & H.J. Lenz (Eds.), Learning from Data: Artificial and Intelligence V
, 1996
"... Experimental comparisons between statistical and machine learning methods appear with increasing frequency in the literature. However, there does not seem to be a consensus on how such a comparison is performed in a methodologically sound way. Especially the effect of testing multiple hypotheses on ..."
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Experimental comparisons between statistical and machine learning methods appear with increasing frequency in the literature. However, there does not seem to be a consensus on how such a comparison is performed in a methodologically sound way. Especially the effect of testing multiple hypotheses on the probability of producing a "false alarm" is often ignored. We transfer multiple comparison procedures from the statistical literature to the type of study discussed in this paper. These testing procedures take the number of tests performed into account, thereby controlling the probability of generating "false alarms". The multiple comparison procedures selected are illustrated on wellknown regression and classification data sets. 26.1 Introduction Recent interactions between the statistical and artificial intelligence communities (see e.g. [Han93, CO94]), have led to many studies that compare the performance of empirical statistical and machine learning methods on reallife data sets; ...
Tutorial in Biostatistics: Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine
"... The general linear mixed model provides a useful approach for analysing a wide variety of data structures which practising statisticians often encounter. Two such data structures which can be problematic to analyse are unbalanced repeated measures data and longitudinal data. Owing to recent advances ..."
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The general linear mixed model provides a useful approach for analysing a wide variety of data structures which practising statisticians often encounter. Two such data structures which can be problematic to analyse are unbalanced repeated measures data and longitudinal data. Owing to recent advances in methods and software, the mixed model analysis is now readily available to data analysts. The model is similar in many respects to ordinary multiple regression, but because it allows correlation between the observations, it requires additional work to specify models and to assess goodnessoffit. The extra complexity involved is compensated for by the additional flexibility it provides in model fitting. The purpose of this tutorial is to provide readers with a sufficient introduction to the theory to understand the method and a more extensive discussion of model fitting and checking in order to provide guidelines for its use. We provide two detailed case studies, one a clinical trial with repeated measures and dropouts, and one an epidemiological survey
Measuring the Adjusted Monetary Base in An Era of Financial Change
, 1996
"... Richard G. Anderson is an assistant vice president and economist at the Federal Reserve Bank of St. Louis. Robert H. Rasche is a professor of economics at Michigan State University and a visiting scholar at the Federal Reserve Bank of St. Louis. We thank Cindy Gleit and Daniel Steiner for excellent ..."
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Richard G. Anderson is an assistant vice president and economist at the Federal Reserve Bank of St. Louis. Robert H. Rasche is a professor of economics at Michigan State University and a visiting scholar at the Federal Reserve Bank of St. Louis. We thank Cindy Gleit and Daniel Steiner for excellent research assistance. We also thank the staff of the Division of Monetary Affairs, Board of Governors of the Federal Reserve System, for providing the data used in this article.
Pharmacodynamic Analysis of Hematologic Profiles
 JOURNAL OF PHARMACOKINETICS AND BIOPHARMACEUTICS
, 1994
"... In this paper, we discuss the analysis of the myelosuppressive effects of chemotherapy. Such analyses examine hematologic data that arise by monitoring patients after treatment with high doses of chemotherapy. We propose an approach for modeling such information and, using data collected as part of ..."
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In this paper, we discuss the analysis of the myelosuppressive effects of chemotherapy. Such analyses examine hematologic data that arise by monitoring patients after treatment with high doses of chemotherapy. We propose an approach for modeling such information and, using data collected as part of a Phase I study of an anticancer agent, show some interesting aspects of the data that become available after fitting models this way.
ME (2003) Functional consequences of genetic diversity in Strongyloides ratti infections
 Proceedings of the Royal Society of London, Series B
"... Parasitic nematodes show levels of genetic diversity comparable to other taxa, but the functional consequences of this are not understood. Thus, a large body of theoretical work highlights the potential consequences of parasite genetic diversity for the epidemiology of parasite infections and its ..."
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Parasitic nematodes show levels of genetic diversity comparable to other taxa, but the functional consequences of this are not understood. Thus, a large body of theoretical work highlights the potential consequences of parasite genetic diversity for the epidemiology of parasite infections and its possible implications for the evolution of host and parasite populations. However, few relevant empirical data are available from parasites in general and none from parasitic nematodes in particular. Here, we test two hypotheses. First, that different parasitic nematode genotypes vary in lifehistory traits, such as survivorship and fecundity, which may cause variation in infection dynamics. Second, that different parasitic nematode genotypes interact within the host (either directly or via the host immune system) to increase the mean reproductive output of mixedgenotype infections compared with singlegenotype infections. We test these hypotheses in laboratory infections using genetically homogeneous lines of Strongyloides ratti. We find that nematode genotypes do vary in their survivorship and fecundity and, consequently, in their dynamics of infection. However, we find little evidence of interactions between genotypes within hosts under a variety of trickleand singleinfected infection regimes.
Exploratory Data Graphics for Repeated Measures Data
"... Repeated measures (RM) is a common data structure in many fields. They are a special form of multivariate data that makes multivariate graphics both practical and useful. A set of basic graphics for RM data is introduced in the context of small to moderately sized balanced data sets where a random e ..."
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Repeated measures (RM) is a common data structure in many fields. They are a special form of multivariate data that makes multivariate graphics both practical and useful. A set of basic graphics for RM data is introduced in the context of small to moderately sized balanced data sets where a random effects model is under tentative consideration. The graphics emphasize looking at as much of the data as possible. Graphical considerations in constructing the plots are discussed. Graphics for checking the mean structure, variance structure and relationship to covariates are mentioned. An approach to modeling is introduced. Key Words: Covariance Selection; Hierarchical Models; Longitudinal Data; Model Specification; Random Effects Models. 1 Introduction Repeated measures (RM) data are multivariate observations where each case Y i = (y i1 ; . . . ; y in i ) consists of repeatedly measuring the same quantity such This work was supported by grant #GM5001101 from NIGM. The idea of plottin...
Conditionally Linear MixedEffects Models With Latent Variable Covariates
"... A version of the nonlinear mixedeffects model is presented that allows random effects only on the linear coefficients. Nonlinear parameters are not stochastic. In nonlinear regression, this kind of model has been called conditionally linear. As a mixedeffects model, this structure is more flexible ..."
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A version of the nonlinear mixedeffects model is presented that allows random effects only on the linear coefficients. Nonlinear parameters are not stochastic. In nonlinear regression, this kind of model has been called conditionally linear. As a mixedeffects model, this structure is more flexible than the popular linear mixedeffects model, while being nearly as straightforward to estimate. In addition to the structure for the repeated measures, a latent variable model (Browne, 1993) is specified for a distinct set of covariates that are related to the random effects in the second level. Unbalanced data are allowed on the repeated measures, and data that are missing at random are allowed on the repeated measures or on the observed variables of the factor analysis submodel. Features of the model are illustrated by two examples. Multilevel models are widely used to study the effects of treatments or to characterize differences between intact groups in designs where individuals are hierarchically nested within random levels of a second variable. Comprehensive reviews of this methodology with emphasis on cluster sampling problems have been presented by Bock (1989), Bryk and Raudenbush (1992), and Goldstein (1987). Essentially the same technology is applied in the analysis of repeated measures. Instead of a model for subjects selected from units of an organization, the prototypical repeated measures design is a series of measurements for a particular individual randomly selected from a population. Recent texts by Crowder and Hand (1990), Davidian and Giltinan (1995), Diggle, Liang, and
Estimating and Predicting Feed Conversion in Broiler Chickens by Modeling Covariance Structure
"... Abstract: Modeling covariance structure was used to estimate and to predict feed conversion in broiler chickens from one experiment under repeatedmeasures design. Eight treatments that consisted in a combination of four strains (Arbor Acres, Ag Ross 308, Cobb and RX) and two sexes were evaluated at ..."
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Abstract: Modeling covariance structure was used to estimate and to predict feed conversion in broiler chickens from one experiment under repeatedmeasures design. Eight treatments that consisted in a combination of four strains (Arbor Acres, Ag Ross 308, Cobb and RX) and two sexes were evaluated at six