Local predictive influence in bayesian linear models with conjugate priors

Cook (1986) presented the idea of local influence to study the sensitivity of inferences to model assumptions:introduce a vector δ of perturbations to the model; choose a discrepancy function D to measure differences between the original inference and the inference under the perturbed model; study the behavior of D near δ = 0, the original model, usually by taking derivatives. Johnson and Geisser (1983) measure influence in Bayesian inference by the Kullback-Leibler divergence between predictive distributions. I~IcCulloch (1989) is a synthesis of Cook and Johnson and Geisser, using Kullback-Leibler divergence between posterior or predictive distributions as the discrepancy function in Bayesian local influence analyses. We analyze a special case for which McCulloch gives the general theory; namely, the linear model with conjugate prior. We present specific formulae for local influence measures for 1) changes in the parameters of the gamma prior for the precision, 2) changes in the mean of the normal prior for the regression coefficients, 3) changes in the covariance matrix of the normal prior for the regression coefficients and 4) changes in the case weights. Our method is an easy way to find locally influential subsets of points without knowing in advance the sizes of the subsets. The techniques are illustrated with a regression example.     

Reliability of global sensitivity indices

Uncertainty and sensitivity analysis is an essential ingredient of model development and applications. For many uncertainty and sensitivity analysis techniques, sensitivity indices are calculated based on a relatively large sample to measure the importance of parameters in their contributions to uncertainties in model outputs. To statistically compare their importance, it is necessary that uncertainty and sensitivity analysis techniques provide standard errors of estimated sensitivity indices. In this paper, a delta method is used to analytically approximate standard errors of estimated sensitivity indices for a popular sensitivity analysis method, the Fourier amplitude sensitivity test (FAST). Standard errors estimated based on the delta method were compared with those estimated based on 20 sample replicates. We found that the delta method can provide a good approximation for the standard errors of both first-order and higher-order sensitivity indices. Finally, based on the standard error approximation, we also proposed a method to determine a minimum sample size to achieve the desired estimation precision for a specified sensitivity index. The standard error estimation method presented in this paper can make the FAST analysis computationally much more efficient for complex models.     

Relevance of Risk Predictions Derived from a Chronic Species Sensitivity Distribution with Cadmium to Aquatic Populations and Ecosystems

Criteria to protect aquatic life are intended to protect diverse ecosystems, but in practice are usually developed from compilations of single‐species toxicity tests using standard test organisms that were tested in laboratory environments. Species sensitivity distributions (SSDs) developed from these compilations are extrapolated to set aquatic ecosystem criteria. The protectiveness of the approach was critically reviewed with a chronic SSD for cadmium comprising 27 species within 21 genera. Within the data set, one genus had lower cadmium effects concentrations than the SSD fifth percentile‐based criterion, so in theory this genus, the amphipod Hyalella, could be lost or at least allowed some level of harm by this criteria approach. However, population matrix modeling projected only slightly increased extinction risks for a temperate Hyalella population under scenarios similar to the SSD fifth percentile criterion. The criterion value was further compared to cadmium effects concentrations in ecosystem experiments and field studies. Generally, few adverse effects were inferred from ecosystem experiments at concentrations less than the SSD fifth percentile criterion. Exceptions were behavioral impairments in simplified food web studies. No adverse effects were apparent in field studies under conditions that seldom exceeded the criterion. At concentrations greater than the SSD fifth percentile, the magnitudes of adverse effects in the field studies were roughly proportional to the laboratory‐based fraction of species with adverse effects in the SSD. Overall, the modeling and field validation comparisons of the chronic criterion values generally supported the relevance and protectiveness of the SSD fifth percentile approach with cadmium.     

Deconstructing maternal sensitivity: Predictive relations to mother‐child attachment in home and laboratory settings

Despite the well‐documented importance of parental sensitivity for child development, there is a lack of consensus regarding how best to assess it. We investigated the factor structure of maternal caregiving behavior as assessed at 12 months by the Maternal Behavior Q‐Sort (Pederson & Moran) with 274 mother‐infant dyads. Subsequently, we examined associations between these empirically‐derived dimensions and child attachment, assessed in the home and laboratory (final N = 157). Three dimensions of maternal behavior were identified, corresponding fairly closely to Ainsworth's original scales. They were labeled Cooperation/Attunement, Positivity, and Accessibility/Availability. Only Cooperation/Attunement consistently predicted home‐based attachment at 15 months and 2 years, and at comparable strength to the overall sensitivity score, suggesting that this construct may be central to sensitivity. At 18 months, compared to their primarily secure counterparts, different types of laboratory‐assessed insecure attachment were associated with different patterns of maternal behavior. Mothers in avoidant relationships (n = 18) were low on Cooperation/Attunement and Accessibility/Availability, but fairly high on Positivity. Mothers of disorganized infants (n = 11) were Cooperative/Attuned but somewhat less Positive toward, and less Accessible/Available to, their infants. A multidimensional approach to parental behavior may facilitate the identification of parenting precursors of insecure parent‐child relationships.     

Evidence-based treatments for trauma among culturally diverse foster care youth: Treatment retention and outcomes

This study describes the implementation of three evidence-based treatments addressing traumatic stress symptoms within a wraparound foster care program in Illinois. Child–Parent Psychotherapy (CPP), Trauma-Focused Cognitive Behavioral Therapy (TF-CBT), and Structured Psychotherapy for Adolescents Responding to Chronic Stress (SPARCS) were implemented with a racially diverse sample of youth ages 3–18 at six agencies. Culturally sensitive adaptations were made to treatment approaches to improve client retention and outcomes. Data analyses revealed no racial differences in retention in the program and no differences in outcomes between minority youth exposed to the intervention and other participants. All three evidence-based treatments were effective in reducing symptoms and improving functioning among minority youth. Implementation issues, including challenges and culturally competent accommodations, are discussed.     

Treatment of Uncertainty in Performance Assessments for Complex Systems

When viewed at a high level, performance assessments (PAs) for complex systems involve two types of uncertainty: stochastic uncertainty, which arises because the system under study can behave in many different ways, and subjective uncertainty, which arises from a lack of knowledge about quantities required within the computational implementation of the PA. Stochastic uncertainty is typically incorporated into a PA with an experimental design based on importance sampling and leads to the final results of the PA being expressed as a complementary cumulative distribution function (CCDF). Subjective uncertainty is usually treated with Monte Carlo techniques and leads to a distribution of CCDFs. This presentation discusses the use of the Kaplan/Garrick ordered triple representation for risk in maintaining a distinction between stochastic and subjective uncertainty in PAs for complex systems. The topics discussed include (1) the definition of scenarios and the calculation of scenario probabilities and consequences, (2) the separation of subjective and stochastic uncertainties, (3) the construction of CCDFs required in comparisons with regulatory standards (e.g., 40 CFR Part 191, Subpart B for the disposal of radioactive waste), and (4) the performance of uncertainty and sensitivity studies. Results obtained in a preliminary PA for the Waste Isolation Pilot Plant, an uncertainty and sensitivity analysis of the MACCS reactor accident consequence analysis model, and the NUREG-1150 probabilistic risk assessments are used for illustration.     

重庆地区奶牛隐性乳房炎病原菌的分离鉴定及药敏试验

本文对重庆地区6个奶牛场的76头黑白花奶牛294份奶样,采用LMT方法检测隐性乳房炎,按常规方法分离鉴定病原菌,用纸片法对其病原菌进行药敏试验。结果表明,LMT阳性奶牛检出率为78.94％、阳性乳区的检出率为50.34％;阳性乳区的细菌检出率为87.83％;分离鉴定的病原菌以葡萄球菌和链球菌为主,占检出细菌总数的94.60％;而这些病原菌对庆大霉素、氯霉素和红霉素的敏感性最高。目前在临床是可首选此类药物治疗奶牛隐性乳房炎。     

低渗储层应力敏感评价方法探讨

应力敏感评价是储层保护以及油气井产能评价的重要依据，尤其对低渗储层更显意义重大。目前国内对油气储层的应力敏感评价方法及标准尚存在争议,通过对国内外曾提出的应力敏感评价     

符号网络分析算法及计算机实现

提出了一种分析电路符号网络函数的新编码算法ＮＳＣＡ。它能有效降低基码，防止过早发生整数溢出，从而扩大分析电路的规模。提出了灵敏度分析算法和化简算法，并可对模块电路进行处理，增强了编码算法的适用范围。根据该算法开发了软件系统ＳＮＳＰＣ２．０。实例运行结果证明了算法理论的正确性和可行性     

Risk of Error and the Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

The accuracy of a binary diagnostic test is usually measured in terms of its sensitivity and its specificity, or through positive and negative predictive values. Another way to describe the validity of a binary diagnostic test is the risk of error and the kappa coefficient of the risk of error. The risk of error is the average loss that is caused when incorrectly classifying a non-diseased or a diseased patient, and the kappa coefficient of the risk of error is a measure of the agreement between the diagnostic test and the gold standard. In the presence of partial verification of the disease, the disease status of some patients is unknown, and therefore the evaluation of a diagnostic test cannot be carried out through the traditional method. In this paper, we have deduced the maximum likelihood estimators and variances of the risk of error and of the kappa coefficient of the risk of error in the presence of partial verification of the disease. Simulation experiments have been carried out to study the effect of the verification probabilities on the coverage of the confidence interval of the kappa coefficient.