Sunk costs have been known to elicit violations of expected utility theory, in particular, the independence or cancellation axiom. Separately, violations of the stochastic dominance principle have been demonstrated in various settings despite the fact that descriptive models of choice favored in economics deem such violations irrational. However, it is currently unknown whether sunk costs also yield stochastic dominance violations. In two studies using a tri-colored roulette wheel choice task with non-equiprobable events yet equal payoffs, we observed that those who had sunk costs selected a stochastically dominated option significantly more than did those who had no costs. Moreover, a second study revealed that people chose a stochastically dominated option significantly more when the expected value was low compared to high. A model comparison of psychological explanations demonstrated that theories that incorporate a reference shift of the status quo could predict these sunk cost-based violations of stochastic dominance whereas other models could not.

Germination and emergence assays represent the most notable examples of time-to-event data in agriculture and related disciplines. In spite of the peculiar characteristics of this type of data, there has been little effort to establish a specific and comprehensive framework for their analyses. Indeed, a brief survey of the literature shows that germination and emergence data, along with other phenological measurements such as flowering time, have been analyzed through myriad approaches, giving rise to confusion and uncertainty among scientists and practitioners as to what may represent the best statistical practice. This lack of coherence in statistical approach may reduce the efficiency of research, while making the communication of results and the cross-study comparisons extremely challenging. Here, we attempt to provide a coherent framework and protocol for the analyses of germination/emergence and other time-to-event data in weed science and related disciplines, together with a software implementation in the form of a new R package. We propose a similar approach to biological assays in ecotoxicology, based on: (1) fitting a time-to-event model to describe the whole time course of events; (2) comparing time-to-event curves across experimental treatments, and (3) deriving further information from the fitted model to better focus on some traits of interest. The most appropriate methods to accomplish this procedure were carefully selected from the framework of survival analysis and related sources and were modified to comply with the specific needs of weed, seed, and plant sciences. Finally, they were implemented in the new R package drcte. In this article, we describe the procedure and its limitations by way of providing examples of several types of germination/emergence assays. We highlight that our proposed procedure can also serve as the first step of data analyses, with its output subsequently submitted to traditional or meta-analytic approaches.

Temporal shaping of time series is the activity of deriving a time series model with a prescribed marginal distribution and some sample path characteristics. Starting with an empirical sample path, one often computes from it an empirical histogram (a step-function density) and empirical autocorrelation function. The corresponding cumulative distribution function is piecewise linear, and so is the inverse distribution function. The so-called inversion method uses the latter to generate the corresponding distribution from a uniform random variable on [0,1), histograms being a special case. This paper shows how to manipulate the inverse histogram and an underlying marginally uniform process, so as to “shape” the model sample paths in an attempt to match the qualitative nature of the empirical sample paths, while maintaining a guaranteed match of the empirical marginal distribution. It proposes a new approach to temporal shaping of time series and identifies a number of operations on a piecewise-linear inverse histogram function, which leave the marginal distribution invariant. For cyclical processes with a prescribed marginal distribution and a prescribed cycle profile, one can also use these transformations to generate sample paths which “conform” to the profile. This approach also improves the ability to approximate the empirical autocorrelation function.

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (−2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

Gravitational lensing is one of most promising tools to probe dark energy and dark matter in our Universe. Lensing by larger-scale structures distorts the shape of background galaxies. For ground-based observations the shape is further distorted by atmospheric turbulence and optical distortions. Many algorithms were proposed to measure the shear signal but the systematic biases are still too large to be acceptable for the larger-sky surveys in the future. I will present our new algorithms for PSF reconstruction and shape measurements based on several sets of basis functions.

Twin studies of child temperament using objective measures consistently suggest moderate heritability for most dimensions. However, parent rating measures produce unusual patterns of results. Intraclass correlations for identical (MZ) twins are typically high, whereas fraternal (DZ) twin intraclass correlations are much lower than would be predicted from an additive genetic model. The ‘too low’ DZ correlations can be explained by parent-rating biases that either exaggerate the differences between DZ twins (contrast effects) or that inflate the similarity of MZ twins (assimilation effects), or by the presence of non-additive genetic variance. To evaluate the three possible explanations, we used model-fitting procedures applied to parent-rating data averaged across 14, 20, 24, and 36 months of age in a sample of 196 twin pairs participating in the MacArthur Longitudinal Twin Study. The data were best described by a model that included contrast effects. Implications for non-twin research are discussed. Twin Research (2000) 3, 224–233.

We studied the influence of mental stress on the contributions of genes and environment to individual variation in systolic (SBP) and diastolic (DBP) blood pressure by structural equation modelling in 320 adolescent male and female twins. Blood pressure data were collected during rest and during a reaction time and a mental arithmetic task. Univariate analyses of SBP and DBP showed familial aggregation for blood pressure. A genetic explanation for this resemblance was most likely, although during rest conditions a model that attributed familial resemblance to shared environmental factors, also fitted the data. There was no evidence for sex differences in heritabilities. Multivariate analyses showed significant heterogeneity between sexes for the intercorrelations of the blood pressure data measured under different rest and task conditions. Multivariate genetic analyses were therefore carried out separately in males and females. For SBP and DBP in females and for SBP in males an increase in heritabilities was seen for blood pressure measured during stress, as compared to rest measurements. The influence of shared environ-mental factors decreased during stress. For DBP in males no significant contributions of shared environment were found. The multivariate analyses indicated that the same genetic and environmental influences are expressed during rest and stress conditions.

The objective of this study was to estimate how much of an individual's appreciation of humour is influenced by genetic factors, the shared environment or the individual's unique environment. A population-based classical twin study of 127 pairs of female twins (71 monozygous (MZ) and 56 dizygous (DZ) pairs) aged 20–75 from the St Thomas' UK Adult Twin Registry elicited responses to five ‘Far Side’ Larson cartoons on a scale of 0–10. Within both MZ and DZ twin pairs, the tetrachoric correlations of responses to all five cartoons were significantly greater than zero. Furthermore, the correlations for MZ and DZ twins were of similar magnitude and in some cases the DZ correlation was greater than that of the MZ twins. This pattern of correlations suggests that shared environment rather then genetic effects contributes to cartoon appreciation. Multivariate model-fitting confirmed that these data were best explained by a model that allowed for the contribution of the shared environment and random environmental factors, but not genetic effects. However, there did not appear to be a general humour factor underlying responses to all five cartoons and no effect of age was seen. The shared environment, rather than genetic factors, explains the familial aggregation of humour appreciation as assessed by the specific ‘off the wall’ cognitive type of cartoons used in this study. Twin Research (2000) 3, 17–22.

Fisheries exploitation systems are characterized by reciprocal interactions between fisheries activity and the harvested resource. Variability and changes affecting ‘natural’ and ‘socio-economic’ environments generate non stationarity of the variables used to describe the fishing activity, the resource and the yields. This characteristic needs to be taken into account in describing the evolution of the relationships between those variables, and in describing fisheries adaptability and sustainability. We present a dynamical system model with an application to data from surveys on activity and results of the Senegalese artisanal fishery from 1974 to 1992. A fitting procedure is proposed, giving estimates of the parameters of the model. A simulation is then made with the fitted model in order to apraise possible impacts of various events of different kind.

Although familial aggregation of blood pressure is well documented, few studies have considered the changing contribution of genetic and environmental influences during adulthood. Applying maximum likelihood model fitting to blood pressure covariation in balanced pedigrees including both parents and their young adult twin offsprings (25 MZ, 32 DZ, aged between 16 and 24 years), it is shown that the increased variation in parents is explained by such developmental changes. For DBP, an apparent reduction in heritability from 68% to 38% from young adulthood to middle age results from the increasing impact of individual environmental experience (E1), with little or no influence from shared family environmental (E2). For SBP, shared environmental effects may play a part. Given the relatively small size of the present sample, the conclusions are to be seen as tentative. An augmented family study, incorporating middle aged twins and their young adult offspring, will clarify the causation of these developmental changes.