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192
Subpathwayminer: A Software Package for Flexible Identification of Pathways
 Nucleic Acids Res
, 2009
"... of pathways ..."
Estimating a bivariate density when there are extra data on one or both components
 Biometrika
, 2006
"... ABSTRACT. Assume we have a dataset, Z say, from the joint distribution of random variables X and Y, and two further, independent datasets, X and Y, from the marginal distributions of X and Y, respectively. We wish to combine X, Y and Z, so as to construct an estimator of the joint density. This prob ..."
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ABSTRACT. Assume we have a dataset, Z say, from the joint distribution of random variables X and Y, and two further, independent datasets, X and Y, from the marginal distributions of X and Y, respectively. We wish to combine X, Y and Z, so as to construct an estimator of the joint density. This problem is readily solved in some parametric circumstances. For example, if the joint distribution were normal then we would combine data from X and Z to estimate the mean and variance of X; proceed analogously to estimate the mean and variance of Y; but use data from Z alone to estimate E(XY). However, the problem is more difficult in a nonparametric setting. There we suggest a copulabased solution, which has potential benefits even when the marginal datasets X and Y are empty. For example, if the copula density is sufficiently smooth in the region where we wish to estimate it, then the effective dimension of the structure that links the marginal distributions is relatively low, and the joint density of X and Y can be estimated with a high degree of accuracy. Similar improvements in performance are available if the marginals are close to being independent. We suggest using wavelet estimators to approximate the copula density, which in cases of statistical interest can be unbounded along boundaries. Our techniques are also useful for solving recentlyconsidered related problems, for example where the marginal distributions are determined by parametric models. Therefore the methodology has application beyond the context which motivated it. The methodology is also readily extended to more general multivariate settings.
Effects of breeding success, mate fidelity and senescence on breeding dispersal of male and female bluefooted boobies
 J Anim
, 2007
"... 1. Understanding the effects of individual and population factors on variation in breeding dispersal (the movement of individuals between successive breeding sites) is key to identifying the strategies behind breeders ’ movements. Dispersal is often influenced by multiple factors and these can be co ..."
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1. Understanding the effects of individual and population factors on variation in breeding dispersal (the movement of individuals between successive breeding sites) is key to identifying the strategies behind breeders ’ movements. Dispersal is often influenced by multiple factors and these can be confounded with each other. We used 13 years of data on the locations, mates, breeding success and ages of individuals to tease apart the factors influencing breeding dispersal in a colonially breeding longlived seabird, the bluefooted booby Sula nebouxii. 2. Breeding dispersal varied among and within years. Males dispersed further in years of higher population density, and late breeding males and females dispersed further than early breeders. This temporal variation related to changes in competition for territory was taken into account in all tests of individual factors influencing breeding dispersal. 3. Individuals that retained their mates from the previous year dispersed shorter distances than those that changed their mates. 4. The effect of previous breeding success depended on mate fidelity. Unsuccessful breeding induced greater dispersal in birds that changed their mates but not in birds that
Characterizing the generalized lambda distribution by Lmoments
 Computational Statistics and Data Analysis
, 2008
"... The generalized lambda distribution (GLD) is a flexible four parametric distribution with many practical applications. Lmoments of the GLD can be expressed in closed form and are good alternatives for the central moments. In this paper, we present the Lmoments of the GLD up to an arbitrary order a ..."
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The generalized lambda distribution (GLD) is a flexible four parametric distribution with many practical applications. Lmoments of the GLD can be expressed in closed form and are good alternatives for the central moments. In this paper, we present the Lmoments of the GLD up to an arbitrary order and study which values of Lskewness and Lkurtosis can be achieved by the GLD. The boundaries of Lskewness and Lkurtosis are derived analytically in the symmetric case and calculated numerically in the general case. In addition, the contours of Lskewness and Lkurtosis are presented as functions of the GLD parameters. It is found that with an exception of the smallest values of Lkurtosis, the GLD covers all possible pairs of Lskewness and Lkurtosis and often there are two or more distributions that share the same Lskewness and the same Lkurtosis. We present an example that demonstrates a situation where there are four GLD members with the same Lskewness and the same Lkurtosis. The results increase our knowledge on the distributions that belong to the GLD family and can be utilized in model selection and estimation. Keywords: skewness, kurtosis, Lmoment ratio diagram, method of moments, method of Lmoments 1 1
Empirical Acquisition of Conceptual Distinctions via Dictionary Definitions
, 2004
"... This thesis discusses the automatic acquisition of conceptual distinctions using empirical methods, with an emphasis on semantic relations. The goal is to improve semantic lexicons for computational linguistics, but the work can be applied to generalpurpose knowledge bases as well. ..."
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This thesis discusses the automatic acquisition of conceptual distinctions using empirical methods, with an emphasis on semantic relations. The goal is to improve semantic lexicons for computational linguistics, but the work can be applied to generalpurpose knowledge bases as well.
SENSITIVITY OF INFERENCES IN FORENSIC GENETICS TO ASSUMPTIONS ABOUT FOUNDING GENES
, 2009
"... Many forensic genetics problems can be handled using structured systems of discrete variables, for which Bayesian networks offer an appealing practical modeling framework, and allow inferences to be computed by probability propagation methods. However, when standard assumptions are violated—for exam ..."
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Many forensic genetics problems can be handled using structured systems of discrete variables, for which Bayesian networks offer an appealing practical modeling framework, and allow inferences to be computed by probability propagation methods. However, when standard assumptions are violated—for example, when allele frequencies are unknown, there is identity by descent or the population is heterogeneous—dependence is generated among founding genes, that makes exact calculation of conditional probabilities by propagation methods less straightforward. Here we illustrate different methodologies for assessing sensitivity to assumptions about founders in forensic genetics problems. These include constrained steepest descent, linear fractional programming and representing dependence by structure. We illustrate these methods on several forensic genetics examples involving criminal identification, simple and complex disputed paternity and DNA mixtures.
Notes ShortTerm Load Forecasting with Neural Network Ensembles: A Comparative Study
"... Load Forecasting plays a critical role in the management, scheduling and dispatching operations ..."
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Load Forecasting plays a critical role in the management, scheduling and dispatching operations
Increasing Informativeness in Temporal Annotation
"... In this paper, we discuss some of the challenges of adequately applying a specification language to an annotation task, as embodied in a specific guideline. In particular, we discuss some issues with TimeML motivated by error analysis on annotated TLINKs in TimeBank. We introduce a document level i ..."
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In this paper, we discuss some of the challenges of adequately applying a specification language to an annotation task, as embodied in a specific guideline. In particular, we discuss some issues with TimeML motivated by error analysis on annotated TLINKs in TimeBank. We introduce a document level information structure we call a narrative container (NC), designed to increase informativeness and accuracy of temporal relation identification. The narrative container is the default interval containing the events being discussed in the text, when no explicit temporal anchor is given. By exploiting this notion in the creation of a new temporal annotation over TimeBank, we were able to reduce inconsistencies and increase informativeness when compared to existing TLINKs in TimeBank. 1
EXPLORING THE STRUCTURE OF MIXTURE MODEL COMPONENTS
 COMPSTAT’2004 SYMPOSIUM
, 2004
"... Modelbased cluster analysis and latent class regression are popular methods for grouping observations into unobserved segments. In many applications it is of great interest to the practitioner to assess the relationships between those segments, especially which segments are close to each other and ..."
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Modelbased cluster analysis and latent class regression are popular methods for grouping observations into unobserved segments. In many applications it is of great interest to the practitioner to assess the relationships between those segments, especially which segments are close to each other and which are markedly different from the rest. We present several new tools for the R statistical computing environment that allow the user to visually explore the component structure of arbitrary mixture models and do computations using a graph representation of the model.