Estimating Noncancer Uncertainty Factors: Are Ratios NOAELs Informative?

The prominent role of animal bioassay evidence in environmental regulatory decisions compels a careful characterization of extrapolation uncertainties. In noncancer risk assessment, uncertainty factors are incorporated to account for each of several extrapolations required to convert a bioassay outcome into a putative subthreshold dose for humans. Measures of relative toxicity taken between different dosing regimens, different endpoints, or different species serve as a reference for establishing the uncertainty factors. Ratios of no observed adverse effect levels (NOAELs) have been used for this purpose; statistical summaries of such ratios across sets of chemicals are widely used to guide the setting of uncertainty factors. Given the poor statistical properties of NOAELs, the informativeness of these summary statistics is open to question. To evaluate this, we develop an approach to calibrate the ability of NOAEL ratios to reveal true properties of a specified distribution for relative toxicity. A priority of this analysis is to account for dependencies of NOAEL ratios on experimental design and other exogenous factors. Our analysis of NOAEL ratio summary statistics finds (1) that such dependencies are complex and produce pronounced systematic errors and (2) that sampling error associated with typical sample sizes (50 chemicals) is non-negligible. These uncertainties strongly suggest that NOAEL ratio summary statistics cannot be taken at face value; conclusions based on such ratios reported in well over a dozen published papers should be reconsidered.     

On Broadening Failure Rate Distributions in PRA Uncertainty Analyses

Several recent nuclear power plant probabilistic risk assessments (PRAs) have utilized broadened Reactor Safety Study (RSS) component failure rate population variability curves to compensate for such things as expert "overvaluation bias" in the estimates upon which the curves are based.
A simple two-components of variation empirical Bayes model is proposed for use in estimating the between-expert variability curve in the presence of such biases. Under certain conditions this curve is a population variability curve. Comparisons are made with the existing method.
The popular procedure appears to be generally much more conservative than the empirical Bayes method in removing such biases. In one case the broadened curve based on the popular method is more than two orders of magnitude broader than the empirical Bayes curve. In another case it is found that the maximum justifiable degree of broadening of the RSS curve is to increase α from 5% to 12%, which is significantly less than the 20% value recommended in the popular approach.     

IMP: A decision aid for multiattribute evaluation using imprecise weight estimates

In multicriteria analysis, weights are inferred from judgements. Judgements are subject to uncertainty and the weights found depend on the method used so that weights must necessarily be imprecise. Describing weights by a suitable probability distribution captures this imprecision and leads to correspondingly imprecise estimates of the multiattribute scores given to alternatives. A method is described which enables the parameters of a Dirichlet distribution to be found from three point weight estimates and for this to be used to determine whether or not alternatives may justifiably be differentiated by their scores. A simple chart shows this information and permits easy interaction. An illustration shows the effects of different sources of imprecision and of aggregating individual assessments in a group.     

Characterizing the Noncancer Toxicity of Mixtures Using Concepts from the TTC and Quantitative Models of Uncertainty in Mixture Toxicity

This article explores the use of an approach for setting default values for the noncancer toxicity, developed as part of the Threshold of Toxicological Concern (TTC), for the evaluation of the chronic noncarcinogenic effects of certain chemical mixtures. Individuals are exposed to many mixtures where there are little or no toxicological data on some or all of the mixture components. The approach developed in the TTC can provide a basis for conservative estimates of the toxicity of the mixture components when compound-specific data are not available. The application of this approach to multiple chemicals in a mixture, however, has implications for the statistical assumptions made in developing component-based estimates of mixtures. Specifically, conservative assumptions that are appropriate for one compound may become overly conservative when applied to all components of a mixture. This overestimation can be investigated by modeling the uncertainty in toxicity standards. In this article the approach is applied to both hypothetical and actual examples of chemical mixtures and the potential for overestimation is investigated. The results indicate that the use of the approach leads to conservative estimates of mixture toxicity and therefore its use is most appropriate for screening assessments of mixtures.     

Producing ratio measures of effect with quantitative microbial risk assessment

Estimating the risk of infections or other outcomes incident to pathogen exposure is a primary goal of quantitative microbial risk assessment (QMRA). Such estimates are useful to predict population-level risks, to evaluate exposures based on normative or tolerable risk guidelines, and to interpret the likely public health relevance of microbial measurements in environmental media. To evaluate alternative control measures (interventions), ratio estimates of effect (e.g., odds and risk ratios) are needed that are more broadly interpretable in the health sciences and consistent with convention in epidemiology. In this paper, we propose a general method for estimating widely used ratio measures of effect derived from stochastic QMRA approaches, including the generation of appropriate confidence intervals. Such QMRA-derived ratios can be used as a basis for evaluating interventions via hypothesis testing and for inclusion in systematic reviews and meta-analyses in a form consistent with risk estimation approaches commonly used in epidemiology.     

基于理性预期均衡框架下的价格泡沫与过度反应研究

基于理性预期均衡框架,分别在私人信息外生和内生获取的条件下研究知情交易者对新信息过度反应与价格泡沫之间的联系。研究发现：在私人信息外生获取的情况下,知情交易者对新信息过度反应会提高价格信息量。在私人信息内生获取的情况下,过度反应挤出了私人信息的生产,导致价格信息量降低,价格泡沫由此形成,此时流动性可能呈先降低后提高的U型变化。这为管理层加强私人信息披露和弱化投资者的过度反应程度提供了依据。     

Optimal Cost Sampling for Decision Making with Multiple Regression Models

This paper develops an explicit relationship between sample size, sampling error, and related costs for the application of multiple regression models in observational studies. Graphs and formulas for determining optimal sample sizes and related factors are provided to facilitate the application of the derived models. These graphs reveal that, in most cases, the imprecision of estimates and minimum total cost are relatively insensitive to increases in sample size beyond n=20. Because of the intrinsic variation of the regression model, even if larger samples are optimal, the relative change in the total cost function is small when the cost of imprecision is a quadratic function. A model-utility approach, however, may impose a lower bound on sample size that requires the sample size be larger than indicated by the estimation or cost-minimization approaches. Graphs are provided to illustrate lower-bound conditions on sample size. Optimal sample size in view of all considerations is obtained by the maximin criterion, the maximum of the minimum sample size for all approaches.     

Shelf Space Management When Demand Depends on the Inventory Level

Two factors that their influence on the demand has been investigated in many papers are (i) the shelf space allocated to a product and to its complement or supplement products and (ii) the instantaneous inventory level seen by customers. Here we analyze the joint shelf space allocation and inventory decisions for multiple items with demand that depends on both factors. The traditional approach to solve inventory models with a state‐dependent demand rate uses a time domain approach. However, this approach often does not lead to closed‐form expressions for the profit rate with both dependencies. We analyze the problem in the inventory domain via level crossing theory. This approach leads to closed‐form expressions for a large set of demand rate functions exhibiting both dependencies. These closed‐form expressions substantially simplify the search for optimal solutions; thus we use them to solve the joint inventory control and shelf space allocation problem. We consider examples with two products to investigate the significance of capturing both demand dependencies. We show that in some settings it is important to capture both dependencies. We consider two heuristics, each one of them ignores one of the two dependencies. Using these heuristics it seems that ignoring the dependency on the shelf space might be less harmful than ignoring the dependency on the inventory level, which, based on computational results, can lead to profit losses of more than 6%. We demonstrate that retailers should use their operational control, e.g., reorder point, to promote higher demand products.     

本地投资者有信息优势吗?基于百度搜索的实证研究

本文以公司注册地所在省份网民在百度中对公司股票简称搜索的次数占全国网民搜索总次数的比例,度量公司被本地个人投资者关注的程度,发现A股市场个人投资者显著过度关注本地公司,即在注意力配置上存在本地偏差。基于注意力配置和资产配置的紧密关联,本文利用这一本地关注度指标对A股市场本地偏差现象源自投资者理性的信息优势还是非理性的行为偏差进行了实证检验,发现个人投资者对本地公司的过度关注提高了上市公司风险溢价水平,强化了个股股价与市场、行业以及区域个股的同步性,并削弱了个股的定价效率,表明行为偏差假说对A股市场本地偏差现象更具解释能力。     

Decision Support by Interval SMART/SWING—Incorporating Imprecision in the SMART and SWING Methods

Interval judgments are a way of handling preferential and informational imprecision in multicriteria decision analysis (MCDA). In this article, we study the use of intervals in the simple multiattribute rating technique (SMART) and SWING weighting methods. We generalize the methods by allowing the reference attribute to be any attribute, not just the most or the least important one, and by allowing the decision maker to reply with intervals to the weight ratio questions to account for his/her judgmental imprecision. We also study the practical and procedural implications of using imprecision intervals in these methods. These include, for example, how to select the reference attribute to identify as many dominated alternatives as possible. Based on the results of a simulation study, we suggest guidelines for how to carry out the weighting process in practice. Computer support can be used to make the process visual and interactive. We describe the WINPRE software for interval SMART/SWING, preference assessment by imprecise ratio statements (PAIRS), and preference programming. The use of interval SMART/SWING is illustrated by a job selection example.     

Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks

The great majority of applications of the popular frontier technique data envelopment analysis (DEA) do not test for the association of efficiency estimates with key performance indicators used by industry observers. Nevertheless, identifying efficiency estimates’ associations with commonly accepted financial measures of performance could guide benchmarking activities, pricing decisions, and regulatory monitoring. Thus, the current paper investigates to what extent bank DEA super-efficiency estimates are associated with key financial ratios. A low correlation may present an opportunity to address inefficiencies that were not obvious in financial ratio analysis, thus enabling an update of inferences drawn from ratios. Regressing ratios on efficiency estimates may also help predict financial ratios. Where an input–output specification is comprised of key financial ratios, DEA can also be used to objectively identify benchmarks for ratio analysis based on actual observed data collected from peers. Nine super-efficiency DEA formulations across two profitability models are systematically tested. The slacks-based measure of DEA with a parsimonious profitability efficiency model emerges as the most significant combination explaining the variation in the two industry ratios, post-tax profit/average total assets and return on average equity.     

基于时变Copula函数的下偏矩最优套期保值效率测度方法研究

由于下偏矩测度方法具有明显优于最小方差风险度量方法的特征,因此是更为合理的套期保值效率测度准则。本文针对已有的计算最小下偏矩套期保值比率的非参数方法与参数方法存在的局限性问题,提出使用时变Copula函数来估计现货与期货收益率的联合密度函数,然后通过数值方法计算最小下偏矩套期保值比率的新方法。并且运用上海期货交易所交易的铜期货合约价格与上海金属网公布的铜现货价格数据进行实证检验,发现使用具有随时间变化的相关系数的Copula函数,与非参数方法相比,可以得到更小下偏矩的套期保值率。     

Combined Effects of Contaminant Desorption and Toxicity on Risk from PAH Contaminated Sediments

A strong inverse correlation was observed between the polycyclic aromatic hydrocarbon (PAH) mass fraction desorbed, a surrogate measure of bioavailability, and relative carcinogenicity, as quantified by potency equivalency factors (PEFs), for two study sediments from the New York/New Jersey Harbor estuary. Because compounds with the highest toxicity, such as dibenz(a,h)anthracene and benzo(a)pyrene (BAP), also tended to be the least rapidly and least extensively desorbed, the U.S. Environmental Protection Agency (EPA) default guidance may dramatically overestimate risk from exposure to PAH-contaminated soils or sediments. A "relative risk index" (RRI) was developed to account for the combined effects of compound-specific bioavailability and toxic potency in estimating excess cancer risk. Using this approach, estimated excess cancer risk may be diminished by as much as a factor of 159 times versus default EPA guidance. Also, the hierarchy of estimated risk between study sediments and among treatment fractions of study sediments differed using the two approaches, implying that the default approach may inaccurately determine site clean-up priorities. The percentage contribution of each potentially carcinogenic priority PAH to total excess cancer risk was computed under various scenarios. In each case, the contribution of BAP to total excess cancer risk was remarkably invariable, for example, ranging from 48% to 52% in one sediment, and 44% to 54% in the other, over four different exposure durations. These results suggest that BAP may be an excellent indexing compound for gauging relative exposure risk across sediments. Other important contributors to total excess cancer risk were benz(a)anthracene and dibenz(a,h)anthracene. Together, these three compounds comprised nearly 90% of total excess cancer risk from all PAHs in every scenario. This integrated RRI approach may enable regulators to more accurately gauge relative risks and make more informed sediment management decisions.     

The Assessment of Probability Distributions from Expert Opinions with an Application to Seismic Fragility Curves

A method is developed for estimating a probability distribution using estimates of its percentiles provided by experts. The analyst's judgment concerning the credibility of these expert opinions is quantified in the likelihood function of Bayes'Theorem. The model considers explicitly the random variability of each expert estimate, the dependencies among the estimates of each expert, the dependencies among experts, and potential systematic biases. The relation between the results of the formal methods of this paper and methods used in practice is explored. A series of sensitivity studies provides insights into the significance of the parameters of the model. The methodology is applied to the problem of estimation of seismic fragility curves (i.e., the conditional probability of equipment failure given a seismically induced stress).     

Errors in Estimates of Smoking-Related Deaths Derived from Nonsmoker Mortality

A number of recent analyses have computed present and future costs associated with a risk by estimating what would happen if the risk were absent. Two sources of bias are associated with this approach: (1) differences in confounding factors between present risk avoiders and risk takers, and (2) the difficulty of selecting an unbiased sample of risk avoiders. A staff memo from the Office of Technology Assessment used this approach to estimate mortality due to smoking. Numbers of deaths and age at death distributions of U.S. smokers and nonsmokers for all causes, all cancers, lung cancers, heart disease, and cerebrovascular lesions are used to assess the accuracy of these estimates. Large errors in the OTA estimates are found. Conditions are discussed that might help reduce errors from this approach.     

Correction Constants for Present Values of Sub-Annual Cash Flows*

When future cash flows are expected to occur at sub-annual intervals, it is widely recognized that present value estimates are biased by the common assumption that each year's flows occur at year-end. Although a “brute force” remedy for the end-of-year (EOY) bias is well known, the method does not appear to be generally used for financial decisions. The author suggests the possible reasons for continued tolerance of EOY bias are that the present method for eliminating the bias is too cumbersome or that the amount of bias is thought to be negligible. The paper also (a) examines the current remedy, indicating several features that hamper its use, (b) presents a correction constant approach that simplifies calculation of unbiased present values for streams of sub-annual cash flows, (c) derives expressions for correction constants under two alternative assumptions about sub-annual discounting, and (d) demonstrates use of the constants and the possible decision-relevance of EOY bias by a simple capital budgeting example.     

Unveiling Variability and Uncertainty for Better Science and Decisions on Cancer Risks from Environmental Chemicals

The National Research Council 2009 “Silver Book” panel report included a recommendation that the U.S. Environmental Protection Agency (EPA) should increase all of its chemical carcinogen (CC) potency estimates by ～7‐fold to adjust for a purported median‐vs.‐mean bias that I recently argued does not exist (Bogen KT. “Does EPA underestimate cancer risks by ignoring susceptibility differences?,” Risk Analysis, 2014; 34(10):1780–1784). In this issue of the journal, my argument is critiqued for having flaws concerning: (1) intent, bias, and conservatism of EPA estimates of CC potency; (2) bias in potency estimates derived from epidemiology; and (3) human‐animal CC‐potency correlation. However, my argument remains valid, for the following reasons. (1) EPA's default approach to estimating CC risks has correctly focused on bounding average (not median) individual risk under a genotoxic mode‐of‐action (MOA) assumption, although pragmatically the approach leaves both inter‐individual variability in CC–susceptibility, and widely varying CC‐specific magnitudes of fundamental MOA uncertainty, unquantified. (2) CC risk estimates based on large epidemiology studies are not systematically biased downward due to limited sampling from broad, lognormal susceptibility distributions. (3) A good, quantitative correlation is exhibited between upper‐bounds on CC‐specific potency estimated from human vs. animal studies (n = 24, r = 0.88, p = 2 × 10^?8). It is concluded that protective upper‐bound estimates of individual CC risk that account for heterogeneity in susceptibility, as well as risk comparisons informed by best predictions of average‐individual and population risk that address CC‐specific MOA uncertainty, should each be used as separate, complimentary tools to improve regulatory decisions concerning low‐level, environmental CC exposures.     

MEASURING INTERTEMPORAL SUBSTITUTION: THE IMPORTANCE OF METHOD CHOICES AND SELECTIVE REPORTING

I examine 2,735 estimates of the elasticity of intertemporal substitution in consumption (EIS) reported in 169 published studies. The literature shows strong selective reporting: researchers discard negative and insignificant estimates too often, which pulls the mean estimate up by about 0.5. The reporting bias dwarfs the effects of methods, with the exception of the choice between micro and macro data. When I correct the mean for the bias, for macro estimates I get zero, even though the reported t‐statistics are on average two. The corrected mean of micro estimates of the EIS for asset holders is around 0.3–0.4. Calibrations greater than 0.8 are inconsistent with the bulk of the empirical evidence.     

Estimation of Jump Tails

We propose a new and flexible nonparametric framework for estimating the jump tails of Itô semimartingale processes. The approach is based on a relatively simple‐to‐implement set of estimating equations associated with the compensator for the jump measure, or its intensity, that only utilizes the weak assumption of regular variation in the jump tails, along with in‐fill asymptotic arguments for directly estimating the “large” jumps. The procedure assumes that the large‐sized jumps are identically distributed, but otherwise allows for very general dynamic dependencies in jump occurrences, and, importantly, does not restrict the behavior of the “small” jumps or the continuous part of the process and the temporal variation in the stochastic volatility. On implementing the new estimation procedure with actual high‐frequency data for the S&P 500 aggregate market portfolio, we find strong evidence for richer and more complex dynamic dependencies in the jump tails than hitherto entertained in the literature.