bluuebunny/crossref_metadata_2025_split huggingface dataset:
Equivalence Trials and Equivalence Limits
Authors: James Hung H. M. 
Published: September 2014

Abstract
An equivalence trial employs a selected active control treatment as a comparator for evaluating the effect of a test treatment. The notion of equivalence is that the difference between the test treatment and the active control is clinically insignificant with respect to therapeutic benefits or an adverse effect at issue. The threshold of clinical insignificance or indifference must be defined and is often termed “equivalence margin” or “equivalence limit.” In clinical trials, the study question that is more often of primary interest is the noninferiority hypothesis that the test treatment is clinically acceptably inferior to the control. Determination of the noninferiority hypothesis with a specified margin often requires special considerations pertaining to other study objectives, such as showing that the test treatment retains a certain fraction of the control's effect. These special considerations in defining a noninferiority margin may not be applicable to defining the superiority margin in the equivalence hypothesis.
Confirmatory Trials
Authors: Joseph Donahue Rafe Michael 
Published: September 2014

Abstract
Confirmatory clinical trials are defined by the ICH as adequately controlled trials in which the hypotheses are stated in advance and evaluated. They can be used to demonstrate or confirm efficacy, establish a safety profile, investigate benefit/risk, or establish dose‐response. They always have (at least) one prespecified primary objective. Confirmatory trials are not exploratory trials.
Biased‐Coin Randomization
Authors: Smith Mike D. 
Published: September 2014

AbstractThe biased coin randomisation is introduced as a procedure to minimize study bias whilst maintaining treatment balance throughout the trial. The range of biased coin randomizations are described for 2 and more than 2 treatment groups, and also for equal and unequal randomization. Biased coin randomizations are described which use both fixed and adaptive levels of coin ‘bias’. The well‐known urn model is described as a special representation of the biased coin randomization, and the link is discussed. Finally, the use of the biased coin randomization procedure for balance within covariate strata, and the application to response adaptive randomizations are described.
Treatment Interruption
Authors: Grund Birgit 
Published: September 2014

AbstractInterruption of active drug treatment in clinical trials can be classified broadly as therapeutic treatment interruption (TI), in which the potential therapeutic benefit of supervised interruption of treatment is investigated, and analytic TI, in which participants are randomized to stopping versus continuing active treatment; the TI arm serves as control group. Design issues for therapeutic TI trials are discussed in the example of a trial in HIV/AIDS.Analytic TIs are used in therapeutic HIV vaccine trials, in randomized discontinuation designs, and in other situations. The use of a randomized discontinuation design for a Phase II drug trial in cancer is discussed.
Sample Size Calculation for Comparing Means
Authors: Wang Hansheng , Chow Shein‐Chung 
Published: September 2014

Abstract
In this article, the focus will be on sample size calculation based on a continuous study endpoint for comparing means (i.e., treatment effects) under some study designs. These designs include parallel designs, crossover designs, titration designs, and enrichment designs (2). Among these designs, parallel designs and crossover designs are probably the two mostly commonly used designs.
Hierarchical Bayesian Space‐Time Analysis
Authors: Berliner L. Mark 
Published: September 2014

Abstract
This article introduces the hierarchical Bayesian (HB) approach to modeling the spatial‐temporal structure and dependence as well as the model uncertainty. Many problems in the environmental sciences involve inferences for space‐time‐varying physical processes, which can be summarized in three stages: Data model, process model and prior on parameters. The HB approach allows for combining observations with physical models.
Conditional Simulation
Authors: Shafer John M. 
Published: September 2014

Abstract
Conditional simulation, also referred to as stochastic imaging, is a stochastic simulation process whereby equally probable realizations of a spatial random function are created with each realization honoring discrete data values at specific locations or geometric patterns, i.e. ‘conditioned’ on the data. Conditional simulations of a random function satisfy two exactitude constraints, the local exactitude of honoring data values and the global exactitude of reproduction of a specified spatial correlation structure. The random variable may be categorical such as an indicator of the presence or absence of some feature (e.g. rock type), or spatially continuous such as porosity or ore grade. Conditional simulation is often used to address problems associated with the spatial distribution of physical properties relevant to the mining industry and several fields of earth sciences, especially hydrogeology, contaminant hydrology, meteorology, and gravimetry. Through Monte Carlo analysis, for example, conditional simulation can be used to evaluate the uncertainty in numerical model parameter values and the translated uncertainty in model response.
Genetic Algorithms
Authors: Sheehan N.A. 
Published: September 2014

AbstractForest inventory is an accounting of trees and their related characteristics of interest over a well‐defined land area. It may be compared to census methods for human populations. For example, one of the goals of the periodic decennial census of the US is to enumerate its human population and to retrieve demographic variables such as age, sex and race. This is accomplished by a comprehensive survey of all households in the country. Similarly, forest inventories seek to enumerate the population of trees within a forest and ascertain other information, such as their volume, value, growth and species composition. For all but the smallest tracts of land, complete enumeration of individuals is usually infeasible and survey sampling techniques are required. Unlike human or animal populations, trees are sessile organisms and there is no immigration or emigration to consider. However, tree populations vary widely in their species composition, age, size, site requirements, potential value, longevity and growth–all factors that may influence the design of a forest inventory. In addition, the tree population of interest may exist over a wide variety of topography, making access potentially costly and sometimes even dangerous. Such variety often dictates sampling protocols that may be optimal only for some portion of the population in question. Alternatively, it may call for varying sampling intensity or strategy (the combination of the sampling design and associated estimators) between different regions within the population.
Binary Data and Quantal Response
Authors: Piegorsch Walter W. 
Published: September 2014

Abstract

Environmental data often occur in discrete form, such as the number of individuals living near a waste site, or whether or not a subject develops cancer after exposure to an environmental agent. This latter example is one of the simplest forms of discrete data: a dichotomous indicator of whether or not an event has occurred, i.e. a ‘success’ or a ‘failure’. Such an outcome can be coded simply as either 1 or 0 – corresponding to success or failure – and we refer to this as a case of
binary data
. If the data on each subject or unit in the study are coded in this binary fashion, and if each subject contributes one and only one such binary observation to the experimental data, we call this a case of
ungrouped binary data
. Alternatively, when the subjects have some underlying or coordinating feature that relates them, it is common to group the like subjects and record the proportion of subjects responding; i.e. take the proportion Y/N, where Y is the number of observed successes out of the N observed subjects in the common group. This is
grouped binary data
.
Bivariate Distributions
Authors: Christman Mary C. 
Published: September 2014

Abstract

Bivariate distributions play a special role in environmental science since they are the underpinnings of analyses of relationships between two random variables. These relationships include such analyses as biserial correlation between continuous and discrete random variables; measures of association in an
r
×
c
contingency table; Pearson's product–moment correlation; Kendall's tau; Spearman's rank correlation; and many others. In addition to studies of the dependency between two variables, interest may center on other analyses; for example, multivariate general (or generalized) linear modeling when two or more response variables are involved. Inferences involving two random variables rely on knowledge of their bivariate distribution. Probably the best known example is estimation and testing of the correlation coefficient in the bivariate normal distribution for two continuous random variables. There are many other distributions of importance as well, especially those relating two discrete random variables or a discrete to a continuous variable. This article describes a few of the more common distributions in environmental practice and, where appropriate, mentions the multivariate distribution of which the bivariate is a special case.
Bivariate Extreme Value Distribution
Authors: Teugels Jozef L. 
Published: September 2014

AbstractIn many situations within environmetrics, investigators deal with bivariate measurements. While each of the components might have its own extreme characteristics, frequently there is a need for insight into the simultaneous happening of two or more extreme events. First a few theoretical aspects are dealt with on how to model the dependence and the location of more information in the literature is indicated. Some specific applications of bivariate extremes within an environmental context are then described.
Inverted Wishart Distribution, Generalized
Authors: Brown Philip J. 
Published: September 2014

Abstract

The generalized inverted Wishart (GIW)
distribution
is a flexible extension of the inverted Wishart (IW) distribution which overcomes the major deficiency of that distribution whilst retaining much of the ease of computation inherent in the natural conjugate roots of the inverted Wishart distribution in Bayesian analysis for normal sampling. In offering a more flexible prior distribution for the covariance matrix of a multivariate normal distribution it has general applicability. It also offers a conjugate Bayesian analysis for problems of missing data where the missing data has a monotone structure. It has been especially useful in simultaneous modeling of the spatial distribution of ozone where monitoring stations come on‐stream in sequence, using multivariate regression analysis for interpolation and prediction.
Mixing Distribution
Authors: Leroux Brian 
Published: September 2014

AbstractA random variableYis said to have a mixture distribution if its probability density (or mass) function can be written as an average over a parametric family of distributions. An example is the Poisson‐gamma distribution, in which conditional on a gamma‐distributed variableM, the variableYhas a Poisson distribution with meanM. The probability distribution assigned to the conditioning parameter (e.g., gamma) is called the mixing distribution. Mixture distributions are particularly useful in environmental and other sciences for modeling overdispersion in discrete data. An extension called the hidden Markov model is available to also account for autocorrelation in discrete data.
Pattern Recognition
Authors: Stein Alfred 
Published: September 2014

AbstractPattern recognition is defined as the automatic identification of figures, characters, shapes, forms and patterns without active human participation in the decision process. These can be structures in one dimension, for example an array of characters, a time series or any other signal, or a structure in two dimensions, such as an image. Higher dimensional patterns are rarely if ever encountered. Pattern recognition can be further divided into recognizing existing features that have a well‐known effect on an image, or the search for coherent subpatterns that may have a common cause. A pattern therefore consists either of features that are known to exist, but are somehow difficult to identify, or of features that may get a specific meaning afterwards (i.e. after they are detected).
Dispersion Parameter
Authors: Piegorsch Walter W. 
Published: September 2014

AbstractTo describe the stochastic nature of an outcome variable, environmetricians use probability distributions. A crucial feature of many simple probability models is that structural relationships may be required between the population mean and other components of the model. Many environmental sampling and experimental settings occur, however, where such a mean/variance structure is too restrictive: the data often exhibit variation larger than or, in some instances, smaller than that required under these simpler models. Such occurrences are known collectively as overdispersion or underdispersion, respectively. We quantify the extent of the over/underdispersion by dedicating a model parameter to it in the variance term. The resultingdispersion parametercan be used to identify, study, test, or otherwise account for the excess variation. This entry reviews statistical aspects of modeling and estimation for such dispersion parameters.
Empirical Bayes Methods
Authors: Louis Thomas A. 
Published: September 2014

AbstractStatistical design, analysis, and reporting are fundamental to environmental research and assessment; to environmetrics. The variety and complexity of applications rapidly grows with a commensurate growth in statistical design and analysis. Environmental applications include all aspects of design, the analysis of spatially structured information (exposure, health outcomes, and demographics), nonlinear dose–response assays, interspecies extrapolation, placement of air or water monitoring stations, remote sensing, and gene–environment interactions. Analytic challenges include stabilizing estimates via statistical modeling and otherwise ‘borrowing information’, integrating information that is misaligned in space and time, dealing with missing data, accounting for measurement error in input information (e.g. exposures), missing data, causal modeling, addressing nonstandard goals and ensuring that statistical evaluations account for the dominant stochastic and modeling uncertainties. In addressing these and other challenges and goals, Bayes and empirical Bayes (EB) designs and analyses have proven valid, efficient, and informative. The beauty of the Bayesian approach is its ability to structure complicated assessments, guide development of appropriate statistical models and inferences, and produce summaries that properly account for uncertainties. Properly structured, these methods can produce objectively valid analyses, commonly more effective than traditional methods. When available, prior information can be integrated into the design and analysis. Computing innovations enable implementation of complex, relevant models and applications burgeon. This article outlines the Bayesian approach, traces the history of the EB approximation, gives a small sampling of environmetric applications and speculates on future developments.
Equivariant Estimation: Theory
Authors: Hallin Marc , JureČková Jana 
Published: September 2014

Abstract
The concepts of invariance and equivariance of estimators with respect to various groups of transformations of the sample space are defined. The structure and properties of equivariant and minimum risk equivariant estimators are characterized. Emphasis is put on equivariance with respect to shift, scale, and affine transformations.
Exact Randomization Technique
Authors: Moir Rob 
Published: September 2014

Abstract

The exact randomization technique is a distribution‐free, computationally‐intensive statistical method of dealing with small samples and uncertain distributions. The technique was first proposed by Fisher in 1935 as a method of dealing with small biological samples. Because of the computational demands, only the recent development of powerful desktop computers permits researchers to regularly use this technique. Suppose two sets of observations are recorded and labeled according to whether they come from a treatment or a control group. The concept of randomization is based on the observation that a conventional test statistic (e.g. a
t
statistic) can be interpreted as a typical draw from the distribution of test statistics that would be generated from randomly shuffling the treatment and control labels among the observed data. In fact, the exact randomization test is essentially a controlled example of bootstrapping.
Order Restricted Inference
Authors: Dykstra Richard , Kuo Hsun‐chih 
Published: September 2014

Abstract
A wide variety of statistical techniques have proven useful in biological and environmental studies. Frequently, these techniques can be improved upon and/or made more powerful if the prior knowledge that certain parameters satisfy an order restriction can be incorporated into the analysis. Moreover, since the existence and implications of orderings can be easily grasped and interpreted by even casual readers and observers, the ability to identify such structure can be especially important. Such structure may exist in many ways. Two possibilities that have been widely discussed are the presence of ordered parameters and the identification of ordered distributions.
PValue and Bayesian Statistics
Authors: Delampady Mohan 
Published: September 2014

AbstractThePvalue is one of the tools from classical statistics that is used very widely in applied sciences. It is also one of the concepts that is highly misunderstood and misused. The basic idea behind Fisher's original (1925) definition ofPvalue did have a great deal of appeal: it is the probability under a (simple) null hypothesis of obtaining a value of a test statistic that is at least as extreme as that observed in the sample data.
Quantiles
Authors: Allen O. Brian 
Published: September 2014

AbstractQuantiles are defined for a sample ofnobservations and for the probability distribution of a population of observations. In many cases, the sample quantile may be viewed as an estimate of the corresponding quantile for a population. Some of the most commonly used quantiles have special names. The first, second and third quartiles divide a probability distribution into four equal parts. Thus, 25% of observations are less than the first quartile and 25% of observations are greater than the third quartile. The second quartile is better known as the median. Other quantiles with special names include the quintiles, which divide a distribution into five equal parts, and percentiles, which divide a distribution into 100 equal parts. Quantiles play an important role in environmental science in a number of ways, examples of which are given in this article.
Robust Estimation of Location and Scale
Authors: Croux Christophe , Dehon Catherine 
Published: September 2014

AbstractSample mean and standard deviation are the classical estimators of the location and scale parameters of a statistical distribution. They are, however, unreliable in the presence of outliers. Robust alternatives have been proposed, of which the median and the median absolute deviation are best known. Several robust estimators of location and scale are reviewed and their properties discussed. The multivariate case is also discussed.
Spectral Methods
Authors: Fuentes Montserrat 
Published: September 2014

Abstract

Spectral methods are a powerful tool for characterizing and modeling spatial dependence, while offering significant computational advantages. Using the spectral representation of a spatial process, we can easily construct valid (positive definite) covariance functions and introduce new models to characterize complex dependence structures of spatial processes. Likelihood approaches for very large spatial datasets are often very difficult, if not infeasible, to implement owing to computational limitations. Even when we can assume normality, exact calculations of the likelihood for a Gaussian spatial process observed at
n
locations requires O(
n
3
) operations. The spectral version of the Gaussian log‐likelihood for gridded data requires O(
n
log2
n
) operations and does not involve calculating determinants.
Competing Risk Analysis
Authors: Gupta Ramesh C. 
Published: September 2014

AbstractIn studies of risk assessment due to environmental exposures, we are interested in the mortality due to a particular disease when several other diseases are acting on a person's life. The method of analyzing survival data in such a population is called “competing risk analysis.” We also have a fundamental assumption that each death takes place due to a single cause. When an individual is put on test, only his time of death and cause of death are recorded. This competing risk data provides insufficient information to determine the joint distribution of the survival times giving rise to an identifiability problem. In this article, the cause‐specific hazard functions are introduced and their estimation and testing, for modeling, both independent and dependent lifetimes are discussed. The identifiability problem is also addressed and some ways to deal with the competing risk data in the presence of association between the latent survival times are suggested.
Compositional Data
Authors: Billheimer Dean 
Published: September 2014

AbstractCompositional data are vectors of proportions describing the relative contributions of each ofDcategories to the whole. This structure is an essential feature of data in many disciplines: in geology as oxide composition of rocks, in ecology as the relative abundance of plants or animals, in air pollution monitoring as the relative concentrations of different chemical species (among others). Statistical approaches for modeling compositional data exhibiting temporal or spatial correlation structure are described in this article. Because methods for compositions are not widely included in statistics education, a brief introduction is given to data characteristics and analysis techniques for independent observations. The focus is observations with three or more categories (D ≥ 3). With only two categories, compositional data are (essentially) univariate, and can be addressed by means of standard statistical techniques (e.g. logit transform). Since the compositional structure is present in a wide variety of problems, many of the methods summarized in this article have been developed outside the environmental context. The approach is to present methods in a general form, and note when there is special development for an environmental application.
Random Fields, Nonparametric Methods
Authors: Tjøstheim Dag 
Published: September 2014

Abstract
An overview is given of nonparametric methods in random fields including estimation of the density, the autocovariance, the specrum, and nonlinear additive models. The difference between expanding domain asymptotics and infill asymptotics is highlighted. Throughout the article similarities and differences relative to time series modeling are pinpointed.