The Ecology of Risk Taking

We analyze the risk level chosen by agents who have private information regarding their quality. We show that even risk-neutral agents will choose risk strategically to enhance their reputation in the market, and that such choices will be influenced by the mix of other agents' types. Assuming that the market has no strong prior about whether the agents are good or bad, good agents will choose low levels of risk, and bad agents high levels. Empirical evidence is gathered on 2462 firms over 24 years. The results support the model: agents of higher quality have less variable performance.     

Immersed in sfumato: Correggio’s sensory palette and the immersive Renaissance

ABSTRACT

This brief essay examines the multisensory aspects of the painterly technique of sfumato, first through an analysis of Corregio’s interpretation of the myth of Jupiter and Io, then in the broader context of Renaissance approaches to the representation of space. While scholarship has emphasized the optical character of sfumato, aerial and linear perspective, the last part of this essay replaces these approaches in the context of the immersive character of Renaissance arts and suggests parallels with the dissolution of boundaries between art and audience characteristic of modern and contemporary art.     

Correlated Equilibrium in Games with Incomplete Information Revisited

A mistake in “Five legitimate definitions of correlated equilibrium (CE) in games with incomplete information” motivates a re-examination of some extensions of the solution concept that Aumann introduced.     

Living Environment Preferences of the Inhabitants of Istanbul: A Modified Hierarchical Information Integration Model

This paper presents a two-stage research to model the different priorities and expectations about living environment preferences of the inhabitants of Istanbul, Turkey's largest metropolitan with a population of 10 million. In the first stage, a modified hierarchical information integration approach was used to estimate the utilities of different attributes for each individual sampled from the inhabitants of Istanbul. This is a decompositional approach and involves measuring individual preferences. In this study, not only aggregate results are reported but also a segmentation study is carried out to explore the heterogeneity in respondents' preferences. Identification of such segments would help planner offer different planning options to groups with different preferences. Thus, the second stage of this study consists of identifying distinct groups of inhabitants with different preference structures by using cluster analysis. The resulting information is believed to be more useful especially to understand diverging demands of inhabitants of Istanbul.     

Robust Inference in Two‐phase Sampling Designs with Application to Unit Nonresponse

Influential units occur frequently in surveys, especially in business surveys that collect economic variables whose distributions are highly skewed. A unit is said to be influential when its inclusion or exclusion from the sample has an important impact on the sampling error of estimates. We extend the concept of conditional bias attached to a unit and propose a robust version of the double expansion estimator, which depends on a tuning constant. We determine the tuning constant that minimizes the maximum estimated conditional bias. Our results can be naturally extended to the case of unit nonresponse, the set of respondents often being viewed as a second‐phase sample. A robust version of calibration estimators, based on auxiliary information available at both phases, is also constructed.     

Nested Kriging predictions for datasets with a large number of observations

This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often considered to tackle such a problem, but the method suffers from its computational burden when the number of observation points is large. We introduce in this article nested Kriging predictors which are constructed by aggregating sub-models based on subsets of observation points. This approach is proven to have better theoretical properties than other aggregation methods that can be found in the literature. Contrarily to some other methods it can be shown that the proposed aggregation method is consistent. Finally, the practical interest of the proposed method is illustrated on simulated datasets and on an industrial test case with \(10^4\) observations in a 6-dimensional space.     

Bayesian item response model: a generalized approach for the abilities' distribution using mixtures

Traditional Item Response Theory models assume the distribution of the abilities of the population in study to be Gaussian. However, this may not always be a reasonable assumption, which motivates the development of more general models. This paper presents a generalized approach for the distribution of the abilities in dichotomous 3-parameter Item Response models. A mixture of normal distributions is considered, allowing for features like skewness, multimodality and heavy tails. A solution is proposed to deal with model identifiability issues without compromising the flexibility and practical interpretation of the model. Inference is carried out under the Bayesian Paradigm through a novel MCMC algorithm. The algorithm is designed in a way to favour good mixing and convergence properties and is also suitable for inference in traditional IRT models. The efficiency and applicability of our methodology is illustrated in simulated and real examples.     

Combining ROC curves using MAMSE weighted distributions

The receiver operating characteristic (ROC) curve can be used to evaluate the properties of a diagnostic test from the distribution of a variable on the healthy and diseased populations. The minimum averaged mean squared error (MAMSE) weights were developed to handle data from different sources by adjusting the relative contribution of each data source. The authors use the MAMSE weights to infer the ROC curve of a diagnostic test based on raw data from multiple studies. The proposed estimates are consistent and Monte Carlo simulations show favourable finite sample performance. The method is illustrated in a case study where progesterone level is used to detect ectopic pregnancies and abortions from other natural causes. The Canadian Journal of Statistics 46: 298–315; 2018 © 2018 Statistical Society of Canada     

The intergenerational transmission of risk attitudes: Evidence from Burkina Faso

Review of Economics of the Household - This paper investigates the intergenerational transmission of risk attitudes in the context of a low income country with a focus on rural–urban and...