Temporal distance reduces the attractiveness of p-bets compared to $-bets

Although people normally prefer a more certain option over a riskier option of equal expected value, sometimes they are tempted to choose the riskier, but more rewarding one. Such temptation is even stronger when people decide for the distant future as compared with the near future. In Experiments 1 and 2 we showed that increasing temporal distance makes people more likely to choose a high risk $-bet (€400, 0.02;0) over a low risk p-bet (€14, 0.60;0). Furthermore, the risk aversion shift increased proportionally to the time delay and persisted even for long delays (6 months). In Experiment 3, we showed that this temporal effect is associated with a decrease in positive feelings towards the p-bet, and with a decrease in the positive evaluation of the high-probability (60%) of the p-bet, but not with an increase of the positive evaluation of the high-payoff (400 euro) of the p-bet. In Experiment 4, we showed that increasing the salience of the probability feature tended to decrease the temporal effect, while increasing the salience of the prize did not vary the strength of the effect. Results are in line with an affect-based explanation of the temporal effect.     

Positive quadrant dependence tests for copulas

In this paper the interest is in testing the null hypothesis of positive quadrant dependence (PQD) between two random variables. Such a testing problem is important since prior knowledge of PQD is a qualitative restriction that should be taken into account in further statistical analysis, for example, when choosing an appropriate copula function to model the dependence structure. The key methodology of the proposed testing procedures consists of evaluating a “distance” between a nonparametric estimator of a copula and the independence copula, which serves as a reference case in the whole set of copulas having the PQD property. Choices of appropriate distances and nonparametric estimators of copula are discussed, and the proposed methods are compared with testing procedures based on bootstrap and multiplier techniques. The consistency of the testing procedures is established. In a simulation study the authors investigate the finite sample size and power performances of three types of test statistics, Kolmogorov–Smirnov, Cramér–von‐Mises, and Anderson–Darling statistics, together with several nonparametric estimators of a copula, including recently developed kernel type estimators. Finally, they apply the testing procedures on some real data. The Canadian Journal of Statistics 38: 555–581; 2010 © 2010 Statistical Society of Canada     

Robust Estimation for Zero‐Inflated Poisson Regression

Abstract. The zero‐inflated Poisson regression model is a special case of finite mixture models that is useful for count data containing many zeros. Typically, maximum likelihood (ML) estimation is used for fitting such models. However, it is well known that the ML estimator is highly sensitive to the presence of outliers and can become unstable when mixture components are poorly separated. In this paper, we propose an alternative robust estimation approach, robust expectation‐solution (RES) estimation. We compare the RES approach with an existing robust approach, minimum Hellinger distance (MHD) estimation. Simulation results indicate that both methods improve on ML when outliers are present and/or when the mixture components are poorly separated. However, the RES approach is more efficient in all the scenarios we considered. In addition, the RES method is shown to yield consistent and asymptotically normal estimators and, in contrast to MHD, can be applied quite generally.     

Graphical modelling and the Mahalanobis distance

I consider the problem of estimating the Mahalanobis distance between multivariate normal populations when the population covariance matrix satisfies a graphical model. In addition to providing a clear understanding of the dependencies in a multivariate data set, the use of graphical models can reduce the variability of the estimated distances and improve inferences. I derive the asymptotic distribution of the estimated Mahalanobis distance under a general covariance model, which includes graphical models as a special case. Two examples are discussed.     

A Robust and Almost Fully Efficient M-Estimator

Simultaneous robust estimates of location and scale parameters are derived from a class of M-estimating equations. A coefficient p ( p > 0), which plays a role similar to that of a tuning constant in the theory of M-estimation, determines the estimating equations. These estimating equations may be obtained as the gradient of a strictly convex criterion function. This article shows that the estimators are uniquely defined, asymptotically bi-variate normal and have positive breakdown for some choices of p . When p = 0.12 and p = 0.3, the estimators are almost fully efficient for normal and exponential distributions: efficiencies with respect to the maximum likelihood estimators are 1.00 and 0.99, respectively. It is shown that the location estimator for known scale has the maximum breakdown point 0.5 independent of p , when the target model is symmetric. Also it is shown that the scale estimator has a positive breakdown point which depends on the choice of p . A simulation study finds that the proposed location estimator has smaller variance than the Hodges–Lehmann estimator, Huber's minimax and bisquare M-estimators.     

A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models

Mixed effects models and Berkson measurement error models are widely used. They share features which the author uses to develop a unified estimation framework. He deals with models in which the random effects (or measurement errors) have a general parametric distribution, whereas the random regression coefficients (or unobserved predictor variables) and error terms have nonparametric distributions. He proposes a second-order least squares estimator and a simulation-based estimator based on the first two moments of the conditional response variable given the observed covariates. He shows that both estimators are consistent and asymptotically normally distributed under fairly general conditions. The author also reports Monte Carlo simulation studies showing that the proposed estimators perform satisfactorily for relatively small sample sizes. Compared to the likelihood approach, the proposed methods are computationally feasible and do not rely on the normality assumption for random effects or other variables in the model.     

Evaluation of statistical protocols for quality control of ecosystem carbon dioxide fluxes

Summary.  The process of quality control of micrometeorological and carbon dioxide (CO₂) flux data can be subjective and may lack repeatability, which would undermine the results of many studies. Multivariate statistical methods and time series analysis were used together and independently to detect and replace outliers in CO₂ flux data derived from a Bowen ratio energy balance system. The results were compared with those produced by five experts who applied the current and potentially subjective protocol. All protocols were tested on the same set of three 5-day periods, when measurements were conducted in an abandoned agricultural field. The concordance of the protocols was evaluated by using the experts' opinion (mean ± 1.96 standard deviations) as a reference interval (the Bland–Altman method). Analysing the 15 days together, the statistical protocol that combined multivariate distance, multiple linear regression and time series analysis showed a concordance of 93% on a 20-min flux basis and 87% on a daily basis (only 2 days fell outside the reference interval), and the overall flux differed only by 1.7% (3.2 g CO₂ m⁻²). An automated version of this or a similar statistical protocol could be used as a standard way of filling gaps and processing data from Bowen ratio energy balance and other techniques (e.g. eddy covariance). This would enforce objectivity in comparisons of CO₂ flux data that are generated by different research groups and streamline the protocols for quality control.     

Object detection in multi-epoch data

In astronomy multiple images are frequently obtained at the same position of the sky for follow-up coaddition as it helps one go deeper and look for fainter objects. With large scale panchromatic synoptic surveys becoming more common, image co-addition has become even more necessary as new observations start to get compared with coadded fiducial sky in real time. The standard coaddition techniques have included straight averages, variance weighted averages, medians etc. A more sophisticated nonlinear response chi-square method is also used when it is known that the data are background noise limited and the point spread function is homogenized in all channels. A more robust object detection technique capable of detecting faint sources, even those not seen at all epochs which will normally be smoothed out in traditional methods, is described. The analysis at each pixel level is based on a formula similar to Mahalanobis distance.     

Product diversification through licensing: Empirical evidence from Chinese firms

Anecdotal evidence suggests that manufacturing firms are able to engage in product diversification through external technology acquisitions, such as technology licensing. However, there is little empirical evidence regarding this proposition to link inward technology licensing and firms’ product diversification or to suggest when and under what conditions the effect of diversification through this channel may be mitigated or augmented. The current study aims to address this research gap through an empirical investigation of a sample of 141 Chinese manufacturing licensee firms. The results indicate that inward technology licensing is positively related to a firm’s diversification and that this relationship is further moderated by a licensee firm’s R&D expenditure and technological distance relative to the licensor firm.     

Preference reversals with social distances

Preference reversals between choice and valuation are typically studied with probabilities or with time delays. I extend them to social distances and document their existence in this new domain.