bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Natural History of Juvenile or Subacute GM2 Gangliosidosis: 21 New Cases and Literature Review of 134 Previously Reported
Authors: Maegawa Gustavo H. B. , Stockley Tracy , Tropak Michael , Banwell Brenda , Blaser Susan , Kok Fernando , Giugliani Roberto , Mahuran Don , Clarke Joe T.R. 
Published: October 2006

OBJECTIVE. Juvenile GM2 gangliosidosis is a group of inherited neurodegenerative diseases caused by deficiency of lysosomal β-hexosaminidase resulting in GM2 ganglioside accumulation in brain. The purpose of this study was to delineate the natural history of the condition and identify genotype-phenotype correlations that might be helpful in predicting the course of the disease in individual patients.METHODS. A cohort of 21 patients with juvenile GM2 gangliosidosis, 15 with the Tay-Sachs variant and 6 with the Sandhoff variant, was studied prospectively in 2 centers. Our experience was compared with previously published reports on 134 patients. Information about clinical features, β-hexosaminidase enzyme activity, and mutation analysis was collected.RESULTS. In our cohort of patients, the mean (±SD) age of onset of symptoms was 5.3 ± 4.1 years, with a mean follow-up time of 8.4 years. The most common symptoms at onset were gait disturbances (66.7%), incoordination (52.4%), speech problems (28.6%), and developmental delay (28.6%). The age of onset of gait disturbances was 7.1 ± 5.6 years. The mean time for progression to becoming wheelchair-bound was 6.2 ± 5.5 years. The mean age of onset of speech problems was 7.0 ± 5.6 years, with a mean time of progression to anarthria of 5.6 ± 5.3 years. Muscle wasting (10.6 ± 7.4 years), proximal weakness (11.1 ± 7.7 years), and incontinence of sphincters (14.6 ± 9.7 years) appeared later in the course of the disease. Psychiatric disturbances and neuropathy were more prevalent in patients with the Sandhoff variant than in those with the Tay-Sachs variant. However, dysphagia, sphincter incontinence, and sleep problems occurred earlier in those with the Tay-Sachs variant. Cerebellar atrophy was the most common finding on brain MRI (52.9%). The median survival time among the studied and reviewed patients was 14.5 years. The genotype-phenotype correlation revealed that in patients with the Tay-Sachs variant, the presence of R178H and R499H mutations was predictive of an early onset and rapidly progressive course. The presence of either G269S or W474C mutations was associated with a later onset of symptoms along with a more slowly progressive disease course.CONCLUSIONS. Juvenile GM2 gangliosidosis is clinically heterogeneous, not only in terms of age of onset and clinical features but also with regard to the course of the disease. In general, the earlier the onset of symptoms, the more rapidly the disease progresses. The Tay-Sachs and Sandhoff variants differed somewhat in the frequency of specific clinical characteristics. Speech deterioration progressed more rapidly than gait abnormalities in both the Tay-Sachs variant and Sandhoff variant groups. Among patients with the Tay-Sachs variant, the HEXA genotype showed a significant correlation with the clinical course.
Dependence, Tests for
Authors: Hollander Myles 
Published: October 2006

AbstractThis paper is designed to provide a sound introduction for a reasonably well informed reader who is, however, not a specialist in tests for dependence. The paper contains references to many tests but emphasizes the parametric test of independence based on Pearson's sample correlation coefficient*rand certain nonparametric tests based on ranks. The ranks tests are generally preferable to the test based onrin that they have wider applicability, are much less sensitive to outlying observations, are exact under mild assumptions that do not require an underlying bivariate normal* population, and have good efficiency (power) properties.
Binomial Distribution: Sample Size Estimation
Authors: Grevstad Nels 
Published: October 2006

AbstractThe need to estimate the sample size parameternin the binomial distribution arises in many practical problems, such as estimating wildlife population sizes based on sample counts. Attempts to find reasonable estimators can be traced back to the early 1900s, when the method of moments estimator was proposed. But the method of moments estimator, as well as the maximum likelihood estimator, suffers serious shortcomings in terms of performance, not the least of which is a highly unstable behavior. Following a few examples and a brief history of the problem, this entry details some of the difficulties that arise when estimatingn, and presents the results of attempts that have been made to overcome those difficulties either by modifying the method of moments or maximum likelihood estimator, or adopting a Bayesian approach.
Concave and Log‐Concave Distributions
Authors: Sengupta Debasis 
Published: October 2006

Abstract
This article deals with the context, scope, properties, applications and inference of concave and log‐concave distribution functions. These two classes of distributions are compared and contrasted with other related classes. It is shown that such distributions can arise from a variety of situations. Characterizations as well as closure and other properties of these classes are reviewed. Discrete versions of these classes and relation with their continuous counterparts are discussed. Implications of log‐concavity of a distribution function in various fields are mentioned. Sharp inequalities for the concave and log‐concave distribution functions having support on the positive real line are provided. Literature on estimation of concave distribution functions is briefly reviewed.
Step‐Stress Testing
Authors: Gouno Evans 
Published: October 2006

Abstract
Step‐stress accelerated life test have been studied at least since the 1950s. The strategy consists in testing items successively under different levels of stresses. The main advantage of this strategy is to overcome the issue of selecting the proper level of stress that generates enough failures to make inference in classical accelerated life test where the stress is constant. It reduces test time and the variability of the failure times. Various types of data are obtained from step‐stress tests. According the nature of the basic assumptions, different statistical techniques can be used. The following work gives an overview of various approaches to handle step‐stress accelerated life test data.
Ceres Plots
Authors: Berk Kenneth N. , Booth David E. 
Published: October 2006

Abstract
In a multiple linear regression, it is assumed that the dependence on each of the predictors is linear. The partial residual plot has been available as a diagnostic, but it can fail if there are nonlinear relationships among the predictors. The Mallows augmented partial residual plot is effective even if there are quadratic relationships among the predictors. Dennis Cook developed the CERES plot to show a curve in the relationship of the dependent variable to a predictor, in spite of any nonlinear relationships among the predictors.
Correlation, Weighted
Authors: Shieh Grace S. 
Published: October 2006

Abstract
Testing the equality of two population correlation coefficients when the data are bivariate normal and Pearson correlation coefficients are used as estimates of the population parameters is a straightforward procedure covered in many introductory statistics courses. The coefficients are converted using Fisher's z‐transformation with standard errors (N‐3)‐1/2. The two transformed values are then compared using a standard normal procedure. When data are not bivariate normal, Spearman's correlation coefficient rho is often used as the index of correlation. Comparison of two Spearman rhos is not as well documented. Three approaches were investigated using Monte Carlo simulations. Treating the Spearman coefficients as though they were Pearson coefficients and using the standard Fisher's z‐transformation and subsequent comparison was more robust with respect to Type I error than either ignoring the nonnormality and computing Pearson coefficients or converting the Spearman coefficients to Pearson equivalents prior to transformation.
Elliptically Contoured Distributions
Authors: Fang Kai‐Tai 
Published: October 2006

AbstractThe elliptically contoured distribution (ECD for short) is a class of multivariate distributions that are considered as an extension of the multivariate normal distributions. In this entry, we introduce a historical perspective, basic properties, and subclasses of the ECD. The estimation of parameters of an ECD implies the study of elliptical matrix distributions. A brief review on elliptical matrix distributions is given. Some applications of ECD are discussed.
Gamma‐Minimax Inference
Authors: Ruggeri Fabrizio 
Published: October 2006

AbstractΓ‐minimax theory deals with the problem of selecting decision rules under partial prior information on the distribution of the unknown parameter. The selected rule is the one minimizing the supremum of the payoff (Bayes and regret risk in a frequentist approach, and posterior expected loss and regret in a Bayesian one) over the priors in Γ.
Bootstrap
Authors: Datta Somnath 
Published: October 2006

AbstractBootstrap methods, also known as resampling, have become a major component of modern statistical tools. Since the seminal paper by Efron [18], a large body of literature has evolved covering theoretical, methodological, and application aspects of this technology. This article provides a rather brief description of Efron's original proposal and its various generalizations and extensions to more complex data structures.
Geostatistics
Authors: Wackernagel Hans 
Published: October 2006

AbstractGeostatistics is an application of the theory of random functions to spatially distributed data. The methods, initially proposed by Matheron in mining and petroleum exploration, found their way back to mainstream statistics more than a decade ago. The geostatistical methodology can be subdivided into linear and multivariate geostatistics, nonstationary geostatistics, nonlinear geostatistics, geostatistical simulation.
U

‐ and

V

‐Statistics
Authors: Shieh Grace S. 
Published: October 2006

Abstract

This article reviews the development of
U
‐ and
V
‐statistics from mid‐1990s to 2001. Three categories of new results are reviewed: (1) asymptotic behavior of
U
‐ and
U
‐related statistics, (2) asymptotic distribution theory (including asymptotic distribution, Berry‐Esseen theorem, and Edgeworth expansion), and (3) a new subclass,
U
‐statistics* with a kernel of indeterminate degeneracy.
Banking, Statistics in
Authors: Hand David J. 
Published: October 2006

AbstractMathematical models in the investment banking and financial markets sectors have been the focus of concentrated research effort for several decades, but the application of statistical methods is a relatively new development. In contrast, in retail banking, statistical models have been applied for over 40 years, though of late their level of sophistication and complexity has increased dramatically. Both developments are a result of increased computer power. A large range of types of statistical tools are being applied, but especially tools concerned with predicting default risk at various levels. Given the increased competition, the current harsh economic climate, and toughening legislative environment, it seems likely that the demand for objective statistical methods will become even greater in the future.
Human Capital
Authors: Dagum Camilo 
Published: October 2006

Abstract
This entry deals with the concept of human capital and its monetary value estimation. The basic assumptions and limitations of the traditional prospective (actuarial) and retrospective (cost of production) methods are discussed. A more efficient estimation procedure, the latent variable‐actuarial method, is presented and illustrated using information from the 1983 US Federal Reserve Board sample survey of income and wealth distribution. The main results obtained are the standardized value of each household HC latent variable, the average HC by age of the household heads, the average national HC, the monetary value of each household HC, and its size distribution.
Stochastic Resonance
Authors: Bulsara Adi R. 
Published: October 2006

AbstractNoise is usually considered a nuisance or a laboratory curiosity, better avoided, in most practical applications. However, certain nonlinear dynamic systems in nature (the most ubiquitous of these being, of course, the central nervous system) appear to utilize the background noise as an aid to the analysis of low‐intensity signals embedded in the noise floor. A phenomenon known as stochastic resonance (SR) has been studied in the context of this amplification of weak signals in a noise background; the effect is only one of a plethora of diversified behavior that is displayed by nonlinear dynamic systems in the presence of noise, and can actually lead to an energy transfer from the noise floor to the signal feature (in the response), under the appropriate operating conditions.
Epidemiological Statistics—II
Authors: McNeil Don 
Published: October 2006

AbstractEpidemiological studies use statistical methods to understand the factors that cause, reduce, and prevent diseases in human populations. These studies estimate the parameters in a target population based on representative samples, and can be qualitative or quantitative, descriptive or comparative, and observational or experimental. Parameters of interest include levels of disease and associations between risk factors and disease outcomes. Concept maps are useful for showing causal paths between risk factors, disease outcomes, and intervening variables (confounders) that could distort the association. The statistical methods can be classified by the type and role of the data, and are subject to both inaccuracy or lack of statistical power (due to sampling variability) and bias (due to faulty measurement, differential selection of subjects, or confounding). Matching of subjects can improve accuracy. Mantel–Haenszel methods can reduce confounding bias. Meta‐analysis is used to combine results from different studies. Statistical models include linear, logistic, and Poisson regression, and the proportional hazards model for handling censored data.
Income Distribution Models
Authors: Dagum Camilo 
Published: October 2006

Abstract
Since Pareto specified his Type I model in 1895, scores of income distribution (ID) models have been proposed. Many of them follow Edgeworth's approach, starting with a probability function and a transformation of its random variable, to derive a positive asymmetric distribution as an ID model. Some of them are supported by a realistic scientific foundation, others are the outcome of a formal analogy, and the remainder are only ad hoc specifications. Almost all of these models are organized here in three generating systems, and their respective foundations are outlined. We introduce a set of seven basic properties to be fulfilled by a probability distribution for consideration as an ID model. Their main purpose is to guide researchers in the evaluation and choice of an appropriate and robust model to describe observed IDs in different epochs and countries in different states of socioeconomic development, and according to several socioeconomic attributes of the observed populations of economic units.
Markov Networks
Authors: Castillo Enrique , Hadi Ali S. 
Published: October 2006

AbstractMany practical applications require the specification of the joint probability density (JPD) of a large set of random variables. This task can be very difficult in practice. Bayesian and Markov networks are two graphically based models that make the specification of the JPD and the propagation of uncertainty simple and efficient because they exploit the conditional independence structure of the JPD. This entry deals with Markov networks. Bayesian networks are discussed in another entry. Markov networks are defined and the questions about how Markov networks can be constructed, how Markov networks can be learnt from data, and how uncertainty can be propagated when new evidence becomes available are also discussed.
Functional Networks
Authors: Castillo Enrique , Hadi Ali S. 
Published: October 2006

AbstractFunctional networks (FNs) are a general framework useful for solving a wide range of problems in various fields. In FNs, the network structure is not necessarily a black box; it is derived from the reality being modeled, that is, it is design‐driven. Unlike in neural networks (NNs), the neural functions in FNs are true multiargument, and different for all neurons. In FNs, neural functions are learned instead of weights as in NNs. In addition, neural functions can be forced to output the same values, thus leading to a system of functional equations that impose strong conditions on the neuron functions and allow simplification of the topology. FNs are illustrated by an example of application.
DNA Microarray Data, Statistical Analysis of
Authors: Datta Susmita 
Published: October 2006

AbstractModern microarray technology has revolutionized the state of biomedical research. With the advent of this technology, biologists can now observe the simultaneous expression profiles of thousands of genes in a single experiment. The large volume of data generated from these experiments has created a need for the development of appropriate statistical tools. Typically, such data sets contain high dimensional multivariate observations with few independent replications. We attempt to give a brief account of the developments of statistical methods in this fast growing area.
Missing Data: Sensitivity Analysis
Authors: Molenberghs Geert 
Published: October 2006

AbstractWith the growing amount of methodology to analyze incomplete data, the awareness has grown that the results of such an analysis may be very sensitive to the modeling assumptions made, many of which are not, strictly speaking, verifiable from the data. Therefore, a lot of the later work is devoted to the exploration of such sensitivities. For both categorical as well as continuous outcomes, we provide a broad definition for such sensitivity analyses, give an overview of methods, and provide some detail about a number of strategies that have become available.
Graph‐Theoretical Methods
Authors: Quiroz Adolfo J. 
Published: October 2006

AbstractThe use of graph‐theoretical structures, principally the Minimal Spanning Tree and thekNearest Neighbors Graph, allows the multivariate generalization of certain nonparametric statistical procedures and suggests the consideration of new ones. The areas of statistical application of graph theoretic procedures include the two‐sample problem, clustering, outlier identification, hypothesis testing, and dimension identification.
Flexible Designs
Authors: Posch Martin , Bauer Peter , Brannath Werner 
Published: October 2006

AbstractFlexible designs allow for midtrial design adaptations in an adaptive interim analysis based on all information collected while controlling the type I error rate. Examples of such adaptations are, for example, sample‐size reassessment, addition or dropping of interim analyses, or dropping of treatment arms. The crucial feature of the adaptive procedure is that the adaptation rules need not be specified in advance.
Stability Study Designs
Authors: Daphne Lin Tsae‐Yun , Chen Chi Wan 
Published: October 2006

Abstract
A good stability study design is the key to a successful stability program. In this article, stability designs including full, bracketing, and matrixing designs will be exemplified. A full design involves complete testing of all factor combinations at all time points, which can be costly. The pharmaceutical industry would like to apply bracketing or matrixing design so that not every factor combination will be tested at every time point. The statistical aspects of these stability designs will be discussed in relation to the present ICH guidelines. Several commonly used criteria for design comparison will be presented. The statistical and regulatory considerations on the selection of stability design will also be discussed. The stability study should be well designed so the shelf life of the drug product can be estimated with a high degree of accuracy and precision.
Occupancy Distributions
Authors: Charalambides Ch. A. 
Published: October 2006

AbstractOccupancy distributions are defined on the stochastic model of random allocation of balls of different kinds intondistinguishable urns. The reduction of the joint distribution of the occupancy numbers, when a specific number ofmballs are allocated, to the joint conditional distribution of independent random variables given their sum, when the number of balls allocated is unspecified, is a powerful technique in the study of occupancy distributions. Using this technique, the distribution of the numberKiof urns occupied byiballs each is derived in terms of the distribution of the independent random variables. In particular, the distribution of the numberK = n − K0of occupied urns is deduced and the distribution of the numberWkof balls required until the occupancy of a predetermined numberkof urns is obtained. Several applications and a generalization of the occupancy model are presented.
Stirling Numbers and Generalizations
Authors: Charalambides Ch. A. 
Published: October 2006

Abstract

The Stirling numbers of the first and second kind enumerate the permutations of a finite set that are decomposed into a given number of cycles and the partitions of a finite set into a given number of subsets, respectively. Basic properties of these numbers and their generalizations of probabilistic and statistical interest are reviewed. Specifically, explicit expressions, generating functions, recurrence relations, and other useful properties of these numbers are presented. Further, the Lah numbers and the generalized factorial coefficients, closely related to the Stirling numbers, are discussed. The noncentral and the associated Stirling and related numbers are briefly presented. The generalized Stirling and Lah numbers and, in particular, the
q
‐Stirling and
q
‐Lah numbers are only quoted. Probabilistic and statistical applications of the Stirling and related numbers in expressing the probability mass function of certain (a) urn‐model distributions, (b) occupancy distributions, (c) convolutions of truncated discrete distributions and (d) compound discrete distributions are exemplified.
Chernoff‐Savage Theorem
Authors: Sen Pranab K. 
Published: October 2006

AbstractThe Chernoff‐Savage Theorem, in the setting of two independent samples, exploits the basic asymptotic properties of the two sample (empirical) distributions in a novel approach to the study of asymptotic normality of a general class of two‐sample linear rank statistics. Prior to the evolution of weak convergence of probability measures and contiguity of probability measures, both taking place in the 1960’s, this Theorem provided a completely new avenue of research in asymptotics in rank tests in a far more general set‐up comprising general linear models, time‐series, and multivariate as well as sequential models
Digamma and Trigamma Distributions
Authors: Sibuya Masaaki 
Published: October 2006

Abstract
Digamma distributions are mixture distributions* of logarithmic series distributions*, and hence heavy‐tailed distributions*, which have two parameters. The distributions vary widely with the parameter values; if one parameter is larger, the distribution is close to the logarithmic series distributions*. If this parameter is near zero, the distribution is reduced to trigamma distributions, which are close to zeta distributions* or Zipf's law*.