bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sample Size Calculation for Comparing Variabilities
Authors: Wang Hansheng , Chow Shein‐Chung 
Published: September 2014

Abstract
In clinical research, in addition to comparing mean responses between treatment groups, it is also of interest to compare variabilities of responses between treatment groups. In practice, it is a concern that (1) larger variability may result in a worse safety profile and (2) larger variability may decrease the probability of reproducibility of the observed clinical results. Besides, efficacy and safety assessment are often done based on mean responses by assuming that the associated variabilities are the same, which may not be true in many cases. As a result, comparing variabilities of responses between treatment groups has received much attention lately. In order to achieve a desired statistical power when comparing variabilities of responses between treatment groups, commonly used procedures or formulas, which are derived based on comparing mean responses, cannot be applied directly and are necessarily modified.
Predicting Random Effects in Community Intervention
Authors: J. Stanek Edward 
Published: September 2014

AbstractThe main objective in a group randomized trial is a comparison between treatments. Often, interest exists in the expected response for a particular group receiving a treatment. As a random sample of groups is included in the trial, the parameter for such a group is represented as a random effect. In this context, predicting the expected response for a group requires predicting a random effect. Proposed solutions to this problem are reviewed focusing on differences in the model assumptions in a simple setting. The author concludes that although popular computing solutions exist, fundamental open questions remain that impact interpretation.
Combination Therapy
Authors: Darbyshire Janet 
Published: September 2014

Abstract
Combination therapy regimens most commonly combine two or more drugs which have additive or synergistic action to treat a disease more effectively, frequently infections or cancer, with the aim of producing more effective regimens and preventing the emergence of resistance of the micro‐organism or the tumour to the drugs. However combinations of drugs are widely used to treat many other diseases such as hypertension, asthma, diabetes and arthritis. Combinations of drugs and other modalities, such as surgery or radiotherapy, are also used in certain disease areas, such as cancer.
Multiple Endpoints
Authors: Bretz Frank , Branson Michael 
Published: September 2014

AbstractThe analysis of multiple endpoints is common practice in pharmaceutical research. A vast amount of literature exists on the design and the analysis of clinical trials with multiple endpoints. Related statistical approaches can roughly be classified into endpoint‐specific multiple testing methods and multivariate global tests. This article briefly summarizes the main approaches, describes their advantages and disadvantages, and provides some recommendations on practical issues.
Nested Experimental Designs
Authors: Shah Kirti R. , Sinha Bikas K. 
Published: September 2014

Abstract
Nested experimental designs form an important subclass of experimental designs wherein some factors are found to be naturally nested within the ‘levels’ of other factors. In a study to examine differential effects of several methods of reducing water pollution, for example, water samples may be taken from different counties and in each county possibly from different water sources, say wells. These samples are then treated with the available methods (for reducing water pollution) and the resulting data analyzed. In this case we may consider the counties as being the levels of the first fac
Spatial Design, Optimal
Authors: Zimmerman Dale L. 
Published: September 2014

Abstract
Statistical inferences from spatial data are affected substantially by the spatial configuration of the sites where those data are observed. Thus, it is natural to try to select a configuration that optimizes, in some sense, the quality of the desired inference(s). This article lays out the classical framework for optimal model‐based spatial sampling design. Prediction error variance design criteria are featured, but optimality criteria for estimating model parameters are also mentioned. Optimal designs may be built “from scratch” or by expanding or contracting an existing design.
Weighted Distributions
Authors: Patil G.P. 
Published: September 2014

Abstract
Traditional environmetric theory and practice have been occupied largely with randomization and replication. But in environmental and ecological work, observations also fall in the nonexperimental, nonreplicated, and nonrandom categories. The problems of model specification and data interpretation then acquire special importance and great concern. The theory of weighted distributions provides a unifying approach for these problems. Weighted distributions take into account the method of ascertainment, by adjusting the probabilities of actual occurrence of events to arrive at a specification of the probabilities of those events as observed and recorded. Failure to make such adjustments can lead to incorrect conclusions.
Nelder Plots
Authors: Barry Ronald P. 
Published: September 2014

Abstract
A Nelder plot, also called a Nelder Wheel or Nelder Fan, is a systematic planting design in which plants or trees are planted at the intersection of circular arcs and linear spokes. In general, Nelder plots allow many different planting densities to be examined in a single plot. This is frequently more efficient than planting a different plot for each planting density. Nelder plots can be constructed that allow the effect of different planting geometries to be examined in a single plot. This article discusses the construction of Nelder plots and the analysis of data derived from Nelder plots.
Effect Size
Authors: Umbach David M. 
Published: September 2014

Abstract
Broadly construed, effect size is the degree to which one distribution differs from another. While the term itself has connotations of cause and effect, the underlying idea applies to any comparison between two populations without implying causality. Suppose that one seeks to compare two populations with respect to a specified outcome. Regard the populations as a control and an exposed population. The outcome under study will have some distribution across control individuals and, if the exposure has any effect at all, a different distribution among exposed individuals. Differences between the two distributions could be assessed using any one of a number of criteria or even using several criteria simultaneously. A desire for parsimony suggests the use of a single quantity for summarizing distributional differences. Generically, such a quantity assesses the magnitude of the exposure effect, that is, the effect size.
Generalized
P
Values: Introduction
Authors: Ananda Malwane M.A. , Weerahandi Samaradasa 
Published: September 2014

Abstract

In many statistical problems involving nuisance parameters, conventional statistical methods do not provide exact solutions. For example, when comparing treatments in a one‐way or two‐way analysis of variance (ANOVA) model without the assumption of equal error variances, classical statistical methods do not provide exact solutions. As a result, practitioners often resort to approximate methods or asymptotic methods, which may perform poorly, specifically with small sample sizes. These kinds of situations arise in many environmental statistical applications. In particular, when dealing with small samples, as a result of using weak tests, experimenters often fail to detect significant differences between treatments. The generalized
P
value approach is a recently developed method based on exact probability statements rather than on asymptotic approximations. The generalized
P
value approach is an extension of the classical
P
value approach.
Model Evaluation and Performance
Authors: Beck B. 
Published: September 2014

Abstract
Whether the behavior of a model matches the behavior of the (real) system–sufficiently well–has always been a matter of great interest. In lay terms, these are the essential questions one would like to have answered in seeking to evaluate a model: (i) has the model been constructed of approved materials, i.e. approved constituent hypotheses (in scientific terms)? (ii) does its behavior approximate well that observed in respect of the real thing? (iii) does it work, i.e. does it fulfill its designated task, or serve its intended purpose?
Outliers
Authors: Aelst Stefan 
Published: September 2014

AbstractDatasets frequently contain outliers which are observations that strongly deviate from the pattern in the majority of the data. Detecting outliers and measuring their influence on an estimation procedure is not trivial. In one or two dimensions graphical inspection of the data may be helpful, but in higher dimensions more reliable methods are needed. We discuss robust estimation to obtain estimates that can resist the influence of outliers and related outlier diagnostics to identify the outliers in a dataset.
Spatial and space‐‐time threshold exceedances
Authors: Angulo José M. , Madrid Ana E. 
Published: September 2014

AbstractThreshold exceedance sets, defined by the coordinates where a certain critical level is exceeded, are of interest in many application fields in relation to risk analysis including deophysics, environmental sciences, engineering, astrophysics, finance, and insurance, among many others. In particular, many risk indicators can be formulated based on certain structural properties of threshold exceedance sets.This article is structured in two parts. The first part reviews the two main approaches to describe the stochastic behavior of temporal threshold excesses, namely, the generalized Pareto distribution and nonhomogeneous Poisson processes, and collects references to some recent developments in the spatial and spatiotemporal contexts. The second part refers to some aspects of interest in relation to the structure of spatial threshold exceedance sets: on the one hand, geometrical measures such as the hypervolume and the Euler characteristic, which are related with certain probabilities of extremal occurrences; on the other hand, two different types of point processes, respectively based on centroids of connected components and on boundaryA‐exit points, which can be used depending on the specific structural properties of interest under study. The article concludes with a discussion and an illustration in the spatiotemporal context.
Subset Selection
Authors: Der Laan Paul Van 
Published: September 2014

AbstractAssumek(k ≥ 2) populations are given. The associated independent random variables have continuous distribution functions differing only in their unknown location parameter. The statistical selection goal of subset selection is to select from the given set ofkpopulations a non‐empty subset of these populations so that the selected subset includes with confidence levelP*, withk−1 < P* < 1, the best population. In this context, best population means the population with the largest value of the location parameter. The subset selection approach is described in some detail and some properties are presented.
Censored Data Analysis
Authors: Portnoy Stephen 
Published: September 2014

Abstract
Censoring is a special form of missing data that arises in a wide variety of situations. Censoring occurs whenever the measurement is restricted to some specific range, but the values being measured can lie outside the range. Proper statistical analysis of such data requires specific adjustments, some of which are described here. Serious errors in inference can be made if censoring is not taken into account properly.
Depth
Authors: Serfling Robert 
Published: September 2014

Abstract
Depth functions represent a recently emerging powerful methodology in nonparametric multivariate inference. They provide multivariate notions of order statistics and generate quantile contours, outlyingness functions, and sign and rank functions. There are wide possibilities for constructing such functions, but these narrow considerably when criteria such as affine equivariance or invariance, computational ease, asymptotic behavior, and robustness are applied. We review basic definitions, leading examples, key properties, and selected applications.
Factor Analysis, Dynamic
Authors: Lippi Marco 
Published: September 2014

AbstractWe shortly reviewed small‐ and medium‐sized standard and dynamic factor analysis, and then concentrate on recent studies on high‐dimensional dynamic factor models. The latter allow the construction of consistent estimators of the factor model, which are based on principal components either in the time or in the frequency domain. Though mainly developed within Time Series Econometrics, high‐dimensional factor models are quite appealing for applications to environmental datasets.
Regression Trees
Authors: Leblanc Michael 
Published: September 2014

Abstract
Regression trees are a flexible statistical method for modeling the conditional distribution of a response variable given a vector of predictors or covariates. Trees have several advantages compared with classical parametric regression methods: they are completely nonparametric; they include powerful variable subset selection; they are robust to outliers in the covariate space and are used easily on a wide variety of data structures.
Runs Test: Theory
Authors: Ley Christophe , Paindaveine Davy 
Published: September 2014

AbstractGiven a sequence of two or more types of symbols, a run is defined as a succession of – one or more – identical symbols that are followed and preceded by a different symbol (or by no symbol at all). Runs tests are based on the length of the longest run or on the total number of runs. They figure among the oldest nonparametric procedures, and can be used in various setups. We review the different definitions of runs, their development over the last 100 years, describe the domains of application of runs tests, and discuss their extension to the multivariate case.
Measurement Error
Authors: Stefanski L.A. 
Published: September 2014

Abstract
Recognition that measurement error in the independent (predictor) variable of data used for regression modeling necessitates special methods of model fitting dates back to the late 1800s. In the ensuing 100 years attention was devoted almost entirely to measurement error in linear regression models, at which time advances in computing opened the door to the nonlinear statistical modeling generally, and to the study of measurement error in nonlinear regression modeling in particular. This article contains an introduction to the problem of measurement error in regression using linear regression measurement error models to illustrate key ideas. It includes a discussion of the types of measurement error commonly studied (classical and Berkson, differential and nondifferential) and how those types arise in practice. A summary of the consequences of ignoring measurement error when analyzing data measured with error is presented, with detailed results given for simple and multiple linear measurement error models. Pointers to the literature on both linear and nonlinear measurement error models are provided.
Nonparametric Regression Model
Authors: Opsomer Jean D. 
Published: September 2014

AbstractNonparametric regression is a rapidly growing and exciting branch of statistics, both because of recent theoretical developments and because of more widespread use of fast and inexpensive computers. Many methods are currently available, including kernel‐based methods, regression splines, smoothing splines and wavelet and Fourier series expansions. This article introduces the main types of smoothing methods and discusses the usefulness of nonparametric regression for analyzing datasets, particularly of the types encountered in environmental statistics.
Piecewise Linear Model
Authors: Küchenhoff Helmut 
Published: September 2014

Abstract

In many practical regression‐type problems we cannot fit one uniform regression function to the data, since the functional relationship between the response
Y
and the regressor
X
changes at certain points of the domain of
X
. These points are usually called break points or change points. In such situations, we apply regression models that are obtained by a piecewise definition of the regression function on intervals of the domain of
X
.
Robust Quantile Regression
Authors: JureČková Jana 
Published: September 2014

Abstract

Robust quantile regression is a collection of statistical procedures based on
regression, autoregression and conditional quantiles
and on
regression and autoregression rank scores
. These procedures can replace the classical estimation and testing procedures in the linear regression and autoregression models, based on least squares, in situations when we cannot guarantee that our observations follow the normal distribution. The character of these methods is nonparametric (i.e. they do not assume any specific probability distribution of the observations), and robust (with respect to outliers in the dependent variables). Moreover, the procedures based on the regression rank scores do not depend on an eventual nuisance regression, because the regression rank scores are invariant to the regression parameters, similarly as the ordinary ranks are invariant to the shift in location. Regression quantiles and regression rank scores are dual in the linear programming sense, but their relation also extends the duality of the quantiles (order statistics) and ranks from the location to the linear regression (autoregression) models.
Robust Regression
Authors: Jorgensen Murray 
Published: September 2014

AbstractUnusual observations can have a large effect on least squares fits of regression coefficients. The most common defenses against this problem are residual plots and the diagnostic statistics based on residuals and leverages. These tools are most effective when the dataset is moderate in size and the analyst is in close touch with, or is, the person who collected the data. However, regression diagnostics become very time consuming to use in large data sets. In this situation it may be preferable to use moreresistantestimators that are relatively unaffected by even large changes to a small proportion of the data. In general, resistance must be ‘purchased’ at the cost of some loss of efficiency when the standard model assumptions hold, but these assumptions are seldom tenable in large data sets anyway. Regression diagnostics and robust regression are regarded by some as conflicting solutions to the problem of the sensitivity of standard least squares regression parameter estimates to unusual, possibly erroneous, observations. However, the two approaches can be seen as complementary. Residuals from robust fits can highlight interesting groups of observations not obvious from standard residual plots; leverage diagnostics can supplement the fits of some convenient, but only partially robust, regression methods.
Smoothing
Authors: Kafadar Karen 
Published: September 2014

AbstractThis article describes smoothing as an exploratory tool in data analysis, including an underlying model that motivates the need for smoothing, the goals and characteristics of good smoothers, general classes of smoothers (linear and nonlinear), and illustrations of the value of smoothing. Smoothing is related to the concepts of general fitting, parametric regression, and nonparametric regression, and the smoothers are illustrated as both one‐ and two‐dimensional data sets. Some insight is offered on choosing smoothing parameters, and some resources for computing smoothers and further references are also provided.
Splines in Nonparametric Regression
Authors: Zhang Hao Helen 
Published: September 2014

AbstractThis article is interested in splines as tools for visualizing and analyzing noisy observational data, and so restricts itself to smoothing splines and regression splines. The article first describes the univariate polynomial smoothing spline, which may be thought of as the forerunner of spline functions used in data analysis. It then describes cross‐validation and generalized cross‐validation (GCV) for choosing the smoothing parameter. After briefly describing regression splines, this entry then describes a number of generalizations of the univariate smoothing spline to various domains, which are obtained via the solution of a variational problem. These include the thin plate spline, the histospline, splines on the sphere, vector splines on the sphere, hybrid splines, partial splines, and smoothing spline analysis of variance (ANOVA) models on complex domains. Computational issues are also discussed. The last two sections focus on the topics of variable selection and model selection in nonparametric regression. Various penalized regression with penalty for function shrinkage and selection are described. Publicly available software is mentioned along the way.
Distance Sampling
Authors: Thomas Len , Buckland Stephen T. , Burnham Kenneth P. , Anderson David R. , Laake Jeffrey L. , Borchers David L. , Strindberg Samantha 
Published: September 2014

AbstractDistance sampling is a widely used group of closely related methods for estimating the density and/or abundance of biological populations. The main methods areline‐transect samplingandpoint‐transect sampling. In both cases, observer(s) perform a standardized survey along a series of randomly located lines or points, searching for objects of interest (usually animals or clusters of animals). For each object detected, they record the distance from the line or point to the object. Not all objects will be detected, but a fundamental assumption of the basic methods is that all objects that are actually on the line or point are detected. The key to distance sampling analyses is to use the observed distances to fit adetection functionthat describes how detectability decreases with increasing distance from the transect. The fitted function is used to estimate the average probability of detecting an object; from here, one can readily obtain point and interval estimates for the density and abundance of objects in the survey area. Various extensions to the basic methods allow assumptions to be relaxed, and include methods that integrate capture‐recapture and distance sampling, as well as methods to model spatial variation in density.
Edge Effect
Authors: Stevens Jr Don L. 
Published: September 2014

AbstractA common objective in sampling a population of biological, ecological, or environmental concern is an inference about the mean or total of some attribute per unit area. In many instances, population attributes are measured in the field by compositing information collected on plots of non‐negligible extent. For example, a forest inventory may count the number of trees and measure their basal area on circular plots. Fish population size might be estimated by counting the number of fish in selected stream reaches in a basin. Estimates of total biomass in a region could be obtained by collecting all biomass in randomly located quadrats. Unfortunately, it is well documented that this general method can result in a discrepancy, often callededge‐effect bias, between the expected value of the estimator and the true value of the total. This discrepancy results from lack of coherence between the measured density and the true density stemming from injudicious handling of plots that are near to or actually straddle the domain boundary. Unless such plots are treated appropriately, population elements near the boundary are weighted incorrectly in computing the overall total. Because edges often have properties that are distinctly different from interior properties, giving an incorrect weight to the edges can result in substantial bias. This article gives an explicit definition of edge‐effect bias, provides a sufficient condition for its absence, and discusses techniques to counter it.