Statistical consulting

The paper and the special issue focus on the activity of statistical consulting and its varieties. This includes academic consulting, consulting to and in industry as well as statistics in public media.     

Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.     

Improved model selection criteria for SETAR time series models

The purpose of this paper is threefold. First, we obtain the asymptotic properties of the modified model selection criteria proposed by Hurvich et al. (1990. Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709–719) for autoregressive models. Second, we provide some highlights on the better performance of this modified criteria. Third, we extend the modification introduced by these authors to model selection criteria commonly used in the class of self-exciting threshold autoregressive (SETAR) time series models. We show the improvements of the modified criteria in their finite sample performance. In particular, for small and medium sample size the frequency of selecting the true model improves for the consistent criteria and the root mean square error (RMSE) of prediction improves for the efficient criteria. These results are illustrated via simulation with SETAR models in which we assume that the threshold and the parameters are unknown.     

Some results on optimality in models with heteroscedastic errors from partial optimum designs

This paper studies optimum designs for linear models when the errors are heteroscedastic. Sufficient conditions are given in order to obtainD-, A- andE-optimum designs for a complete regression model from partial optimum designs for some sub-parameters. A result about optimality for a complete model from the optimality for the submodels is included. Supported by Junta de Andalucía, research group FQM244.     

A “brer rabbit” case for reverse mandated benefits

Reverse mandated benefits is a government-mandated policy that requires employees to provide their employers with benefits that workers would not provide otherwise. Of course, only those benefits would be mandated that are worth more to employers than they cost, as determined by political authorities. My case for such a policy argues that it is at least as sensible as policies mandating that employers provide benefits to their employees that would not be provided otherwise.     

Ecocultural Attributes: Evaluating Ecological Degradation in Terms of Ecological Goods and Services Versus Subsistence and Tribal Values

It is becoming increasingly clear that scientists, managers, lawyers, public policymakers, and the public must decide how to value what is provided by, and is a consequence of, natural resources. While “Western” scientists have clear definitions for the goods and services that ecosystems provide, we contend that these categories do not encompass the full totality of the values provided by natural resources. Partly the confusion results from a limited view of natural resources derived from the need to monetize the value of ecosystems and their component parts. Partly it derives from the “Western” way of separating natural resources from cultural resources or values, and partly it derives from the false dichotomy of assuming that ecosystems are natural, and anything constructed by man is not natural. In this article, we explore the previous assumptions, and suggest that because cultural resources often derive from, and indeed require, intact and unspoiled natural ecosystems or settings, that these values are rightly part of natural resources. The distinction is not trivial because of the current emphasis on cleaning up chemically and radiologically contaminated sites, on restoration of damaged ecosystems, on natural resource damage assessments, and on long‐term stewardship goals. All of these processes depend upon defining natural resources appropriately. Several laws, regulations, and protocols depend upon natural resource trustees to protect natural resources on trust lands, which could lead to the circular definition that natural resources are those resources that the trustees feel they are responsible for. Where subsistence or tribal peoples are involved, the definition of natural resources should be broadened to include those ecocultural attributes that are dependent upon, and have incorporated, natural resources. For example, a traditional hunting and fishing ground is less valued by subsistence peoples if it is despoiled by contamination or physical ecosystem degradation; an Indian sacred ground is tarnished if the surrounding natural environment is degraded; a traditional homeland is less valued if the land itself is contaminated. Our argument is that intact natural resources are essential elements of many cultural resources, and this aspect requires and demands adequate consideration (and may therefore require compensation).     

Intertemporal choice under timing risk: An experimental approach

This paper investigates how individuals evaluate delayed outcomes with risky realization times. Under the discounted expected utility (DEU) model, such evaluations depend only on intertemporal preferences. We obtain several testable hypotheses using the DEU model as a benchmark and test these hypotheses in three experiments. In general, our results show that the DEU model is a poor predictor of intertemporal choice behavior under timing risk. We found that individuals are averse to timing risk and that they evaluate timing lotteries in a rank-dependent fashion. The main driver of timing risk aversion is nothing but probabilistic risk aversion that stems from the nonlinear treatment of probabilities.