Nonlinear Relationships (nonlinear + relationships)

Distribution by Scientific Domains

Selected Abstracts


CRIMINOLOGY, Issue 4 2003
Sociologists and criminologists have become increasingly concerned with nonlinear relationships and interaction effects. For example, some recent studies suggest that the positive relationship between neighborhood disadvantage and violent crime is nonlinear with an accelerating slope, whereas other research indicates a nonlinear decelerating slope. The present paper considers the possibility that this inconsistency in findings is partially caused by lack of attention to an important methodological concern. Specifically, we argue that researchers have not been sensitive to the ways in which logarithmic transformation of the dependent variable can bias tests for nonlinearity and statistical interaction. We illustrate this point using demographic and violent crime data for urban neighborhoods, and we propose an alternative procedure to log transformation that involves the use of weighted least-squares regression, heteroscedasticity consistent standard errors, and diagnostics for influential observations. [source]

Assessing early warning signals of currency crises: a fuzzy clustering approach

Shuhua Liu
In the period of 1990s alone, four waves of financial crises occurred around the world. The repeated occurrence of financial crises stimulated a large number of theoretical and empirical studies on the phenomena, in particular studies on the determinants of or early warning signals of financial crises. Nonetheless, the different studies of early warning systems have achieved mixed results and there remains much room for further investigation. Since, so far, the empirical studies have focused on conventional economic modelling methods such as simplified probabilistic models and regression models, in this study we examine whether new insights can be gained from the application of the fuzzy clustering method. The theories of fuzzy sets and fuzzy logic offer us the means to deal with uncertainties inherent in a wide variety of tasks, especially when the uncertainty is not the result of randomness but the result of unknown factors and relationships that are difficult to explain. They also provide us with the instruments to treat vague and imprecise linguistic values and to model nonlinear relationships. This paper presents empirical results from analysing the Finnish currency crisis in 1992 using the fuzzy C-means clustering method. We first provide the relevant background knowledge and introduce the fuzzy clustering method. We then show how the use of fuzzy C-means method can help us to identify the critical levels of important economic indicators for predicting of financial crises. Copyright 2007 John Wiley & Sons, Ltd. [source]

Timing is everything: flexible phenology and shifting selection in a colonial seabird

Thomas E. Reed
Summary 1In order to reproduce successfully in a temporally varying environment, iteroparous animals must exhibit considerable behavioural flexibility across their lifetimes. By adjusting timing of breeding each year, parents can ensure optimal overlap between the energy intensive period of offspring production and the seasonal peak in favourable environmental conditions, thereby increasing their chances of successfully rearing young. 2Few studies investigate variation among individuals in how they respond to fluctuating conditions, or how selection acts on these individual differences, but this information is essential for understanding how populations will cope with rapid environmental change. 3We explored inter-annual trends in breeding time and individual responses to environmental variability in common guillemots Uria aalge, an important marine top predator in the highly variable California Current System. Complex, nonlinear relationships between phenology and oceanic and climate variables were found at the population level. Using a novel application of a statistical technique called random regression, we showed that individual females responded in a nonlinear fashion to environmental variability, and that reaction norm shape differed among females. 4The pattern and strength of selection varied substantially over a 34-year period, but in general, earlier laying was favoured. Females deviating significantly from the population mean laying date each year also suffered reduced breeding success, with the strength of nonlinear selection varying in relation to environmental conditions. 5We discuss our results in the wider context of an emerging literature on the evolutionary ecology of individual-level plasticity in the wild. Better understanding of how species-specific factors and local habitat features affect the timing and success of breeding will improve our ability to predict how populations will respond to climate change. [source]

Prediction of Water,s Mobility and Disorder in Protein Crystals Using Novel Local Hydrophobic Descriptors

Yuzhu Pan
Abstract The B-factors of crystal structures reflect the atomic fluctuations about their average positions and provide important information about molecular dynamics. Although numerous works have been addressed on theoretical and computational studies of B-factor profile of protein atoms, the methods used for predicting B-factor values of water molecules in protein crystals still remain unexploited. In this article, we describe a new approach that we named local hydrophobic descriptors (LHDs) to characterize the hydrophobic landscapes of protein hydration sites. Using this approach coupled with partial least squares (PLS) regression and least-squares squares support vector machine (LSSVM), we perform a systematic investigation on the linear and nonlinear relationships between the LHDs and water B-factors. Based upon an elaborately selected, large-scale dataset of crystal water molecules, our method predicts B-factor profile with coefficient of determination rpred of 0.554. We demonstrate that (i) the dynamics of water molecules is primarily governed by the local features of hydrophobic potential landscapes, and (ii) the accuracy of predicted B-factor values depends on water packing density. [source]

Is life-history buffering or lability adaptive in stochastic environments?

OIKOS, Issue 7 2009
David N. Koons
It is commonly thought that temporal fluctuations in demographic parameters should be selected against because of the deleterious impacts variation can have on fitness. A critical underpinning of this prediction is the assumption that changes in environmental conditions map linearly into changes in demographic parameters over time. We detail why this assumption may often break down and why selection should not always favor buffering of demographic parameters against environmental stochasticity. To the contrary, nonlinear relationships between the environment and demographic performance can produce asymmetric temporal variation in demographic parameters that actually enhances fitness. We extend this result to structured populations using simulation and show that ,demographic lability' rather than ,buffering' may be adaptive, particularly in organisms with low juvenile or adult survival. Finally, we review previous ecological work, and indicate cases where ,demographic lability' may be adaptive, then conclude by identifying research that is needed to develop a theory of life-history evolution that encompasses both demographic buffering and lability. [source]

Mining performance data through nonlinear PCA with optimal scaling

Paola Costantini
Abstract Performance data are usually collected in order to build well-defined performance indicators. Since such data may conceal additional information, which can be revealed by secondary analysis, we believe that mining of performance data may be fruitful. We also note that performance databases usually contain both qualitative and quantitative variables for which it may be inappropriate to assume some specific (multivariate) underlying distribution. Thus, a suitable technique to deal with these issues should be adopted. In this work, we consider nonlinear principal component analysis (PCA) with optimal scaling, a method developed to incorporate all types of variables, and to discover and handle nonlinear relationships. The reader is offered a case study in which a student opinion database is mined. Though generally gathered to provide evidence of teaching ability, they are exploited here to provide a more general performance evaluation tool for those in charge of managing universities. We show how nonlinear PCA with optimal scaling applied to student opinion data enables users to point out some strengths and weaknesses of educational programs and services within a university. Copyright 2009 John Wiley & Sons, Ltd. [source]

Critical thresholds associated with habitat loss: a review of the concepts, evidence, and applications

Trisha L. Swift
A major conservation concern is whether population size and other ecological variables change linearly with habitat loss, or whether they suddenly decline more rapidly below a "critical threshold" level of habitat. The most commonly discussed explanation for critical threshold responses to habitat loss focus on habitat configuration. As habitat loss progresses, the remaining habitat is increasingly fragmented or the fragments are increasingly isolated, which may compound the effects of habitat loss. In this review we also explore other possible explanations for apparently nonlinear relationships between habitat loss and ecological responses, including Allee effects and time lags, and point out that some ecological variables will inherently respond nonlinearly to habitat loss even in the absence of compounding factors. In the literature, both linear and nonlinear ecological responses to habitat loss are evident among simulation and empirical studies, although the presence and value of critical thresholds is influenced by characteristics of the species (e.g. dispersal, reproduction, area/edge sensitivity) and landscape (e.g. fragmentation, matrix quality, rate of change). With enough empirical support, such trends could be useful for making important predictions about species' responses to habitat loss, to guide future research on the underlying causes of critical thresholds, and to make better informed management decisions. Some have seen critical thresholds as a means of identifying conservation targets for habitat retention. We argue that in many cases this may be misguided, and that the meaning (and utility) of a critical threshold must be interpreted carefully and in relation to the response variable and management goal. Despite recent interest in critical threshold responses to habitat loss, most studies have not used any formal statistical methods to identify their presence or value. Methods that have been used include model comparisons using Akaike information criterion (AIC) or t -tests, and significance testing for changes in slope or for polynomial effects. The judicious use of statistics to help determine the shape of ecological relationships would permit greater objectivity and more comparability among studies. [source]