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This paper proposes an approach which simultaneously considers the properties of cost and quality based on the Burr distribution to determine three parameters (including sample size, sampling interval between successive samples, and the control limits) when an x-bar chart monitors a manufacturing process with Weibull failure characteristic and non-normal data. Also, the cost model of Banerjee and Rahim (1988) is used as the objective function, and the probability density function of the Burr distribution is applied to derive the statistical constraints of economic statistical design of the x-bar control charts for non-normal data. The example of Banerjee and Rahim (1988) is adopted to indicate the solution procedure and sensitivity analyses. Meanwhile, the design parameters of the x-bar control charts can be obtained through the grid search method. The results show that an increase of skewness coefficient (η) results in a slight decrease for sample size (η), but is robust to the control limit width (L). Also, an increase of kurtosis coefficient (η) leads to a wider control limit width. [ABSTRACT FROM AUTHOR]

The assessment of groundwater vulnerability to pollution has proved to be an effective tool for water resource management, especially in arid and semi-arid regions like Mahdia and Ksour Essaf. The main objective of this study is to assess the aquifer vulnerability by applying the DRASTIC method as well as using sensitivity analysis to evaluate the effect of each DRASTIC parameter on the final vulnerability map. An additional objective is to demonstrate the role of the GIS techniques in the vulnerability assessment. The DRASTIC method assigns a high vulnerability to the coast of the Mahdia-Ksour Essaf. The lowest values are observed in the southern part of the study area. A sensitivity analysis applied in this study suggests that net recharge, aquifer media and depth of groundwater are the key factors determining vulnerability. The model is validated with groundwater quality data and the results have shown strong relationships between modified DRASTIC Vulnerability Index and nitrate and chloride concentrations. Citation Saidi, S., Bouri, S. & Ben Dhia, H. (2011) Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study. Hydrol. Sci. J. 56(2), 288-304. [ABSTRACT FROM AUTHOR]

The article describes a new methodology for the emulation of high-order, dynamic simulation models. This exploits the technique of dominant mode analysis to identify a reduced-order, linear transfer function model that closely reproduces the linearized dynamic behavior of the large model. Based on a set of such reduced-order models, identified over a specified region of the large model's parameter space, nonparametric regression, tensor product cubic spline smoothing, or Gaussian process emulation are used to construct a computationally efficient, low-order, dynamic emulation (or meta) model that can replace the large model in applications such as sensitivity analysis, forecasting, or control system design. Two modes of emulation are possible, one of which allows for novel 'stand-alone' operation that replicates the dynamic behavior of the large simulation model over any time horizon and any sequence of the forcing inputs. Two examples demonstrate the practical utility of the proposed technique and supplementary materials, available online and including Matlab code, provide a background to the methods of transfer function model identification and estimation used in the article. [ABSTRACT FROM AUTHOR]

The New Zealand coastline faces the risk of tsunami from a variety of sources, including remote and local earthquakes, submarine landslides, and volcanoes. Palaeotsunami studies indicate New Zealand has undergone periods of catastrophic tsunami activity, such as in the 1500s. It is important to be able to quantify the hazard posed by tsunamis. In this paper we detail the process of modelling tsunami propagation and inundation from source to destination using RiCOM, a finite element coastal hydrodynamic model on an unstructured grid. We give examples of model outputs, such as maps of tsunami inundation depth, maximum speeds, wave arrival times and time spent inundated, which can be used to quantify the hazard. We also perform sensitivity analysis of the response of the tsunami to the average slip on, and length and width of, the fault surface while keeping the moment magnitude and all other parameters constant. Larger average slip is shown to produce larger tsunamis. The width/aspect ratio of the slip has a more variable effect on the size of the tsunami, which depends upon the local coastal geometry. This information can be used to identify risk areas, plan escape routes, protect people and infrastructure and, more generally, to inform the public as to the tsunami risk in their region. [ABSTRACT FROM AUTHOR]

We present a numerical model that simulates morphological change as a result of the interactions between hydrodynamics, sediment transport and bed elevation change. Numerical simulations indicate that these morphodynamic interactions can lead to the initiation of tidal channels and potentially give rise to large-scale channel networks. We perform a sensitivity analysis to show how model outcome is sensitive to the numerical scheme adopted, hydrodynamic and morphological time-steps, and initial bathymetry. Furthermore, the formation of tidal channels and intertidal areas affects both the large-scale flow patterns, as well as the asymmetry between flood- and ebb-tidal currents. [ABSTRACT FROM AUTHOR]

An instrument is a random nudge toward acceptance of a treatment that affects outcomes only to the extent that it affects acceptance of the treatment. Nonetheless, in settings in which treatment assignment is mostly deliberate and not random, there may exist some essentially random nudges to accept treatment, so that use of an instrument might extract bits of random treatment assignment from a setting that is otherwise quite biased in its treatment assignments. An instrument is weak if the random nudges barely influence treatment assignment or strong if the nudges are often decisive in influencing treatment assignment. Although ideally an ostensibly random instrument is perfectly random and not biased, it is not possible to be certain of this; thus a typical concern is that even the instrument might be biased to some degree. It is known from theoretical arguments that weak instruments are invariably sensitive to extremely small biases; for this reason, strong instruments are preferred. The strength of an instrument is often taken as a given. It is not. In an evaluation of effects of perinatal care on the mortality of premature infants, we show that it is possible to build a stronger instrument, we show how to do it, and we show that success in this task is critically important. We also develop methods of permutation inference for effect ratios, a key component in an instrumental variable analysis. [ABSTRACT FROM AUTHOR]

Computer models are important tools used in scientific investigations of natural phenomena. A computer model may be viewed as an input → output function. In order to assess the influence of each input on the output, formal sensitivity analyses are typically carried out. A specific type of sensitivity analysis addressed in this article is the functional ANOVA, in which an output variance decomposition is used to quantify each input's importance. Here we propose functional ANOVA for computer models with time series output. The argument given in this article originates in the concept of conditional expectation and variance with respect to time. We also establish a relationship between the proposed sensitivity indices and the global regular sensitivity indices existent in the literature. An application from the automotive industry is presented to illustrate the methods. [ABSTRACT FROM AUTHOR]

The cross-match test is an exact, distribution-free test of no treatment effect on a high-dimensional outcome in a randomized experiment. The test uses optimal nonbipartite matching to pair 2 I subjects into I pairs based on similar outcomes, and the cross-match statistic A is the number of times that a treated subject was paired with a control, rejecting for small values of A. If the test is applied in an observational study in which treatments are not randomly assigned, then it may be comparing treated and control subjects who are not comparable, and thus may falsely reject a true null hypothesis of no treatment effect. We develop a sensitivity analysis for the cross-match test and apply it in an observational study of the effects of smoking on gene expression levels. In addition, we develop a sensitivity analysis for several multiple testing procedures using the cross-match test and apply it to 1627 molecular function categories in Gene Ontology. [ABSTRACT FROM AUTHOR]

In longitudinal clinical trials, when outcome variables at later time points are only defined for patients who survive to those times, the evaluation of the causal effect of treatment is complicated. In this paper, we describe an approach that can be used to obtain the causal effect of three treatment arms with ordinal outcomes in the presence of death using a principal stratification approach. We introduce a set of flexible assumptions to identify the causal effect and implement a sensitivity analysis for nonidentifiable assumptions which we parameterize parsimoniously. Methods are illustrated on quality of life data from a recent colorectal cancer clinical trial. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

For the high-resolution airborne synthetic aperture radar (SAR) stereo geolocation application, the final geolocation accuracy is influenced by various error parameter sources. In this paper, an airborne SAR stereo geolocation parameter error model, involving the parameter errors derived from the navigation system on the flight platform, has been put forward. Moreover, a kind of near-direct method for modeling and sensitivity analysis of navigation parameter errors is also given. This method directly uses the ground reference to calculate the covariance matrix relationship between the parameter errors and the eventual geolocation errors for ground target points. In addition, utilizing true flight track parameters’ errors, this paper gave a verification of the method and a corresponding sensitivity analysis for airborne SAR stereo geolocation model and proved its efficiency. [ABSTRACT FROM AUTHOR]

This paper illustrates the importance of taking into account the potential errors in discharge estimation in the assessment of extreme floods. First, a summary of the main difficulties encountered in extrapolating rating curves for flood discharge is provided. Then a sensitivity analysis is carried out using a hydraulic modelling approach, applied to eight Mediterranean catchments, and yielding an envelope curve for the stage-discharge relationship, Q(H). To assess the influence of errors in the flood discharge on the uncertainty in estimating extreme floods, a Bayesian framework including a multiplicative error on the rating curve was applied. Its application on two catchments for which historical data are available for the period (1741-2004) shows that ignoring the rating curve errors may lead to an unduly optimistic reduction in the final uncertainty in estimation of flood discharge quantiles. Moreover, the quantile values are also affected by taking into account the rating curve errors. Citation Lang, M., Pobanz, K., Renard, B., Renouf, E. & Sauquet, E. (2010) Extrapolation of rating curves by hydraulic modelling, with application to flood frequency analysis. Hydrol. Sci. J. 55(6), 883-898. [ABSTRACT FROM AUTHOR]

An observational study attempts to draw inferences about the effects caused by a treatment when subjects are not randomly assigned to treatment or control as they would be in a randomized trial. After adjustments have been made for imbalances in measured covariates, the key source of uncertainty in an observational study is the possibility that subjects were not comparable prior to treatment in terms of some unmeasured covariate, so that differing outcomes in treated and control groups are not effects caused by the treatment. A sensitivity analysis asks about the magnitude of the departure from random assignment needed to alter the qualitative conclusions of the study, and the power of a sensitivity analysis and the design sensitivity anticipate the outcome of a sensitivity analysis under an assumed model for treatment effect. Lacking theoretical guidance, we tend to select statistical methods for use in observational studies based on their efficiency in randomized experiments. This turns out to be a mistake. A highly efficient method for detecting small treatment effects in randomized experiments need not, and often does not, have the highest power in a sensitivity analysis or the largest design sensitivity. That is, the best procedure assuming the observational study is a randomized experiment need not be the best procedure under more realistic assumptions. Small effects are sensitive to small biases, and methods targeted at detecting small effects in the absence of bias may not give due emphasis to evidence that the effect is stable and not small, and hence not easily attributed to small or moderate biases. The issue is illustrated in a practical example, is explored informally using a graphical heuristic, is studied asymptotically by determining the design sensitivity for a signed rank statistic with general scores, and is evaluated in a simulation. Among robust procedures with similar Pitman efficiencies for several distributions, there are large differences in design sensitivity and hence substantial differences in the power of a sensitivity analysis. [ABSTRACT FROM AUTHOR]

We address the problem of deciding between two actions related to the rate of faults in a process of manufacturing many identical items. The faults occur independently of each other and are rare, so that the binomial distribution of their count in a given period is well approximated by a Poisson. We use prior information in the form of a set of so-called plausible prior distributions to reflect the difficulties and uncertainty inherent in the process of capturing the expert's knowledge and opinions in a format amenable to a Bayesian analysis. Our analysis also incorporates an elicited set of plausible loss functions. [ABSTRACT FROM AUTHOR]

In this study, we report the development and validation of a duplex real-time polymerase chain reaction (PCR) assay with an internal control using TaqMan-labelled probes for the detection of Mycoplasma gallisepticum and Mycoplasma synoviae (duplex MGMS PCR). The MGMS PCR was highly specific with a sensitivity of 7 and 1 colony-forming units/ml for M. gallisepticum and M. synoviae, respectively, using dilution of pure culture that corresponds to 34 and 29 DNA copies per reaction. Validation of the assay was completed with 260 and 27 pooled samples (tracheal swabs) from commercial chickens and turkeys, respectively, with potential M. gallisepticum and M. synoviae involvement and 42 samples (palatine cleft swabs) from backyard geese and ducks. Using isolation as the gold standard, the MGMS PCR was more sensitive than isolation and the analytical sensitivity was 0.944 and 0.958 for M. gallisepticum and M. synoviae, respectively. In comparison with a gapA-based assay (gapA PCR) and a 16S rRNA-based assay (16S PCR) for M. gallisepticum and M. synoviae, respectively, the results agreed for 94.5% and 96.6%, respectively. The use of the internal control allowed monitoring of proper extraction and inhibition of amplification that was detected in 12 samples. The duplex MGMS PCR was shown to be superior to the presently reported real-time PCR assays in terms of combination of sensitivity, specificity and capacity of detection of more than one target in a single tube. In conclusion, the duplex MGMS PCR was highly specific, sensitive, and reproducible and could be used on clinical samples from commercial chickens, turkeys and backyard poultry including ducks and geese. [ABSTRACT FROM AUTHOR]

In experimental testing, it is desirable to select combinations of input factors that yield optimal results given an objective specified by a practitioner. Often this objective involves minimizing the uncertainty in a parameter or prediction estimate. To determine the experimental design to achieve such an objective, it is necessary to understand the relationship between the testing variables and the response. Alphabetic optimal designs are commonly used in such applications based largely on the ease of their construction with advanced statistical software. However, many publications cite concerns with the overall robustness of such designs to small departures in parameter estimates. This paper examines the issue of model parameter sensitivity to the selection of an accelerated life test based on the UC-optimality criterion, minimizing of the prediction variance at the u,sage condition, using a generalized linear model framework. We will examine the trade-off implications of choice of experimental design, sample size, and censoring. [ABSTRACT FROM AUTHOR]

Efficient planning and design of an appropriate reverse logistics network is crucial to the economical collection and disposal of scrapped household appliances and electrical products. Such systems are commonly modelled as mixed-integer programs, whose solutions will determine the location of individual facilities that optimize material flow. One of the major drawbacks of current models is that they do not adequately address the important issue of uncertainty in demand and supply. Another deficiency in current models is that they are restricted to a two-echelon system. This study addresses these deficiencies by embodying such uncertainties in the model using the technique of fuzzy-chance constrained programming, and by extending the model to a three-echelon system. A heuristic in the form of a hybrid genetic algorithm is then employed to generate lowcost solutions. The overall objective is to find economical solutions to the general problem of determining the volume of appliances to be moved between the three echelons of customer base to collection sites, collection sites to disposal centres and disposal centre to landfill centre/remanufacturing centre; and to the problems of positioning the disposal centres and the landfill centre/remanufacturing centres within the problem domain. A case example in China is presented and the quality and robustness of the solutions are explored through sensitivity analysis. . [ABSTRACT FROM AUTHOR]

Here we analyze body mass index (BMI) data for children and adolescents from the Third National Health and Nutrition Examination Survey (NHANES III). Because of the lack of BMI values for a considerable number of the children and adolescents, and the differential probabilities of selection of these individuals, serious nonresponse and selection bias in inference can be present. To analyze the NHANES III BMI data, a nonignorable nonresponse model has been proposed to estimate the finite population means of small domains formed by crossing age, race and sex within counties. In this approach, the log-BMI values are used to obtain more normally distributed data, and the model includes a spline regression of log-BMI on age, adjusted for race, sex, and the interaction of race and sex. In this work, to assess the status of overweight and obesity in children and adolescents, our new model predicts the more informative finite population percentiles of BMI for these small domains, incorporating additional measures to minimize possible biases. These measures are the most appropriate transformation for the skewed BMI data, incorporation of an intraclass correlation within the households, and inclusion of selection probabilities into the nonignorable nonresponse model to reflect the higher probabilities of selection among black non-Hispanics and Hispanic-Americans. We also consider robustness and sensitivity to the assumptions of the nonignorable nonresponse model by fitting several versions of our proposed model, as well as a very different ignorable nonresponse model that uses a mixture of Student t densities, selection probabilities, and BMI values. It is noteworthy that in the likelihood-based inference literature, we have seen no work by others that includes both nonignorable nonresponse and nonignorable selection. Based on NHANES III data, we show that there are differences in the 85th or 95th percentile for overweight by county, race, and particularly age, and a small difference in sex. [ABSTRACT FROM AUTHOR]

Constructing musculoskeletal models of extinct vertebrates requires subjective assumptions about soft tissue parameters rarely preserved in the fossil record. Despite these necessary assumptions about fundamental input values, paleobiologists rarely perform objective tests of best-estimate models before reaching conclusions based on predicted results. The extent to which lack of knowledge on soft tissue anatomy limits the accuracy of running speed estimates of extinct dinosaurs is therefore poorly understood. In this study, a sensitivity analysis is performed on an evolutionary robotics model of the non-avian theropod dinosaur Allosaurus, used previously to estimate maximum running speed in this extinct animal. A range of muscle parameters were varied over the range observed in extant vertebrates, whereas mass-related parameters were altered across the range of published estimates for Allosaurus. Muscle parameters have a linear relationship with maximum running speed, whereas surprisingly total body mass and torso center of mass have little effect. Muscle force values produced the greatest range in predicted running speeds (4.5–10.7 m/s) and stride lengths (4–5.8 m) in the sensitivity analysis, equating to 65.9% and 30.7% variation about the original 'best-estimate' prediction, a relatively high potential margin of error. These results highlight the importance of sensitivity analyses in biomechanical modeling of extinct taxa, particularly where values for soft tissues parameters are not tightly constrained. The current range in plausible values for soft tissue properties makes a robust quantitative assessment of behavioral ecology and species interactions in dinosaurian communities extremely difficult. [ABSTRACT FROM AUTHOR]

A sensitivity analysis displays the increase in uncertainty that attends an inference when a key assumption is relaxed. In matched observational studies of treatment effects, a key assumption in some analyses is that subjects matched for observed covariates are comparable, and this assumption is relaxed by positing a relevant covariate that was not observed and not controlled by matching. What properties would such an unobserved covariate need to have to materially alter the inference about treatment effects? For ease of calculation and reporting, it is convenient that the sensitivity analysis be of low dimension, perhaps indexed by a scalar sensitivity parameter, but for interpretation in specific contexts, a higher dimensional analysis may be of greater relevance. An amplification of a sensitivity analysis is defined as a map from each point in a low-dimensional sensitivity analysis to a set of points, perhaps a "curve," in a higher dimensional sensitivity analysis such that the possible inferences are the same for all points in the set. Possessing an amplification, an investigator may calculate and report the low-dimensional analysis, yet have available the interpretations of the higher dimensional analysis. [ABSTRACT FROM AUTHOR]

Sensitivity indexes when the inputs of a model are not independent are derived from lbcal polynomial techniques. Two original estimators based on local polynomial smoothers are proposed. Both have good theoretical properties, which are illustrated through analytical examples. Comparison with the Bayesian approach developed by Oakley and O'Hagan (2004) is also performed. The two proposed estimators are used to carry out a sensitivity analysis on two real case models with correlated parameters. [ABSTRACT FROM AUTHOR]

An observational or nonrandomized study of treatment effects may be biased by failure to control for some relevant covariate that was not measured. The design of an observational study is known to strongly affect its sensitivity to biases from covariates that were not observed. For instance, the choice of an outcome to study, or the decision to combine several outcomes in a test for coherence, can materially affect the sensitivity to unobserved biases. Decisions that shape the design are, therefore, critically important, but they are also difficult decisions to make in the absence of data. We consider the possibility of randomly splitting the data from an observational study into a smaller planning sample and a larger analysis sample, where the planning sample is used to guide decisions about design. After reviewing the concept of design sensitivity, we evaluate sample splitting in theory, by numerical computation, and by simulation, comparing it to several methods that use all of the data. Sample splitting is remarkably effective, much more so in observational studies than in randomized experiments: splitting 1,000 matched pairs into 100 planning pairs and 900 analysis pairs often materially improves the design sensitivity. An example from genetic toxicology is used to illustrate the method. [ABSTRACT FROM AUTHOR]

In randomized experiments, it is Sometimes important to demonstrate that two treatments do not differ greatly in their effects, or to demonstrate that the treatments have very different effects on one outcome but similar effects on another outcome. These "demonstrations" may take the form of rejecting a null hypothesis of inequivalence in an equivalence test, or rejecting a null hypothesis of equal and inequivalent effects on two outcomes. The procedures often express a complex hypothesis in terms of component hypotheses, and combine the component significance levels to test the complex hypothesis. If used in a randomized trial, randomization provides valid significance levels for each component test, and hence also for the combined procedure. In an observational study-that is, in a study of treatments that were not randomly assigned-there is typically concern that significance levels for testing hypotheses about treatment effects may be distorted by failure to control for some unobserved pretreatment covariate. This concern is raised in the evaluation of virtually all observational studies. The possible impact of such an unobserved covariate is clarified and displayed by a sensitivity analysis that, for various possible magnitudes of potential bias, yields a corresponding interval of possible significance levels. Some observational studies are sensitive to very small unobserved biases, whereas others are insensitive to large biases. Here, existing sensitivity analyses for component hypotheses are used to generate sensitivity analyses for complex hypotheses tested by combining component significance levels. We apply the procedure to our study of the timing of discharges of premature babies from neonatal intensive care units, focusing on the possible impact of delayed discharge on use of unplanned medical care after discharge. [ABSTRACT FROM AUTHOR]

Housing facilities design represents the main strategic decision in pig farms. This paper introduces a linear programming formulation of a semi-Markov process to approach the facilities design. Thus, the Linear Programming formulation determines the optimum replacement policy and provides the equilibrium distribution of the herd along pig facilities. Then, the calculation of the associated needs of room for each sow facility is derived from sow herd distribution at equilibrium. Results show the flexibility of the model for designing pig facilities and computational advantages in the solving procedure compared to previous proposals. Furthermore, the robustness of the optimal solution is studied by means of sensitivity analysis. [ABSTRACT FROM AUTHOR]

Abstract: This work represents is another contribution to the application of optimal control theory to production inventory systems. We consider a firm that produces some product at a certain rate and aims at improving this rate. We successfully formulated the model as an optimal control problem and obtained an explicit solution using the maximum principle. An illustrative example is provided and the sensitivity of the model to some of the system parameters was addressed. [Copyright &y& Elsevier]

In 2003 Thompson and colleagues reported that daily use of finasteride reduced the prevalence of prostate cancer by 25% compared to placebo. These results were based on the double-blind randomized Prostate Cancer Prevention Trial (PCPT), which followed 18,882 men with no prior or current indications of prostate cancer annually for 7 years. Enthusiasm for the risk reduction afforded by the chemopreventative agent and adoption of its use in clinical practice, however, was severely dampened by the additional finding in the trial of an increased absolute number of high-grade (Gleason score ≥ 7) cancers on the finasteride arm. The question arose as to whether this finding truly implied that finasteride increased the risk of more severe prostate cancer or was a study artifact due to a series of possible postrandomization selection biases, including differences among treatment arms in patient characteristics of cancer cases, differences in biopsy verification of cancer status due to increased sensitivity of prostate-specific antigen under finasteride, differential grading by biopsy due to prostate volume reduction by finasteride, and nonignorable dropout. Via a causal inference approach implementing inverse probability weighted estimating equations, this analysis addresses the question of whether finasteride caused more severe prostate cancer by estimating the mean treatment difference in prostate cancer severity between finasteride and placebo for the principal stratum of participants who would have developed prostate cancer regardless of treatment assignment. We perform sensitivity analyses that sequentially adjust for the numerous potential postrandomization biases conjectured in the PCPT. [ABSTRACT FROM AUTHOR]

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An instrument manipulates a treatment that it does not entirely control, but the instrument affects the outcome only indirectly through its manipulation of the treatment. The idealized prototype is the randomized encouragement design, in which subjects are randomly assigned to receive either encouragement to accept the treatment or no such encouragement, but hot all subjects comply by doing what they are encouraged to do, and the situation is such that only the treatment itself, hot disregarded encouragement alone, can affect the outcome. An instrument is weak if it has only a slight impact on acceptance of the treatment, that is, if most people disregard encouragement to accept the treatment. Typical applications of instrumental variables are not ideal; encouragement is not randomized, although it may be assigned in a far less biased manner than the treatment itself. Using the concept of design sensitivity, we study the sensitivity of instrumental variable analyses to departures from the ideal of random assignment of encouragement, with particular reference to the strength of the instrument. With these issues in mind, we reanalyze a clever study by Angrist and Krueger concerning the effects of military service during World War II on subsequent earnings, in which birth cohorts of very similar but not identical age were differently "encouraged" to serve in the war. A striking feature of this example is that those who served earned more, but the effect of service on earnings appears to be negative; that is, the instrumental variables analysis reverses the sign of the naive comparison. For expository purposes, this example has the convenient feature of enabling, by selecting different birth cohorts, the creation of instruments of varied strength, from extremely weak to fairly strong, although separated by the same time interval and thus perhaps similarly biased. No matter how large the sample size becomes, even if the effect under study is quite large, studies with weak instruments are extremely sensitive to tiny biases, whereas studies with stronger instruments can be insensitive to moderate biases. [ABSTRACT FROM AUTHOR]

Climate change has important implications for assessing impacts of many types of project. If climate change is to be included in environmental assessments, then proponents must be able to incorporate its impacts and inherent uncertainties effectively into their analysis; many proponents do not possess sufficient grounding in how to accomplish this task successfully. In this paper, three basic analytical approaches to uncertainty analysis -- scenario analysis, sensitivity analysis, and probabilistic analysis -- are presented that proponents could use for integrating climate change induced impacts and their uncertainties into their environmental assessments, together with a framework for judging the circumstances that determine which method would be applicable. The use of these three approaches is illustrated on the environmental impacts of a run-of-the-river hydroelectric project. [ABSTRACT FROM AUTHOR]

Drawing inferences about the effects of treatments and actions is a common challenge in economics, epidemiology, and other fields. We adopt Rubin's potential outcomes framework for causal inference and propose two methods serving complementary purposes. One can be used to estimate average causal effects, assuming no confounding given measured covariates. The other can be used to assess how the estimates might change under various departures from no confounding. Both methods are developed from a nonparametric likelihood perspective. The propensity score plays a central role and is estimated through a parametric model. Under the assumption of no confounding, the joint distribution of covariates and each potential outcome is estimated as a weighted empirical distribution. Expectations from the joint distribution are estimated as weighted averages or, equivalently to first order, regression estimates. The likelihood estimator is at least as efficient and the regression estimator is at least as efficient and robust as existing estimators. Regardless of the no-confounding assumption, the marginal distribution of covariates times the conditional distribution of observed outcome given each treatment assignment and covariates is estimated. For a fixed bound on unmeasured confounding, the marginal distribution of covariates times the conditional distribution of counterfactual outcome given each treatment assignment and covariates is explored to the extreme and then compared with the composite distribution corresponding to observed outcome given the same treatment assignment and covariates. We illustrate the methods by analyzing the data from an observational study on right heart catheterization. [ABSTRACT FROM AUTHOR]

Spatial deterministic simulation models such as DaisyGIS are increasingly being used for decision-making in European planning. In Denmark specifically, there is an increasing interest at the regional level to use DaisyGIS as an administrative tool. There are, however, uncertainties associated with the use of these deterministic models, partly due to the spatial variation in input parametres. This study evaluates the use of DaisyGIS for nitrate leaching assessments based on nitrate leaching experiments carried out at the Marbjerg experimental site at Roskilde University. The study includes geostatistical analysis of soil and hydraulic variables and the use of remote sensing for deriving spatial information about the Leaf Area Index. It is found that the model predicts nitrate leaching well when using site-specific input data but it is quite sensitive to the parameterization of carbon, the C/N ratio and variations in the Leaf Area Index (LAI). With the increasing use of Daisy in the counties, there is a need to find better ways of handling uncertainties in model output which can be used by decision-makers and to study further the impact of spatial LAI assimilation on catchment scale nitrate leaching processes. [ABSTRACT FROM AUTHOR]

This article considers a robust Bayesian approach to the sample size determination problem. We focus on global Bayesian robustness that studies lower bound (Ln), upper bound (Un), and range (Rn) of posterior quantities of interest, obtained as the prior varies in a class of distributions. Specifically, we are interested in the selection of an appropriate sample size that gives guarantees to the researcher of observing a small value of the range and, depending on the problems, either a sufficiently large lower bound or a sufficiently small upper bound. Toward this end, we approach the problem as a design issue and provide new sample size determination criteria based on summaries of the predictive distributions of Ln, Un, and Rn, such as expectations and tail probabilities. Relationships and comparison to standard classical and (nonrobust) Bayesian methods are discussed. The proposed methods are studied for the normal model with conjugate priors and used for choosing the size of a clinical trial. [ABSTRACT FROM AUTHOR]

The effects of vaccine on postinfection outcomes, such as disease, death, and secondary transmission to others, are important scientific and public health aspects of prophylactic vaccination. As a result, evaluation of many vaccine effects condition on being infected. Conditioning on an event that occurs posttreatment (in our case, infection subsequent to assignment to vaccine or control) can result in selection bias. Moreover, because the set of individuals who would become infected if vaccinated is likely not identical to the set of those who would become infected if given control, comparisons that condition on infection do not have a causal interpretation. In this article we consider identifiability and estimation of causal vaccine effects on binary postinfection outcomes. Using the principal stratification framework, we define a postinfection causal vaccine efficacy estimand in individuals who would be infected regardless of treatment assignment. The estimand is shown to be not identifiable under the standard assumptions of the stable unit treatment value, monotonicity, and independence of treatment assignment. Thus selection models are proposed that identify the causal estimand. Closed-form maximum likelihood estimators (MLEs) are then derived under these models, including those assuming maximum possible levels of positive and negative selection bias. These results show the relations between the MLE of the causal estimand and two commonly used estimators for vaccine effects on postinfection outcomes. For example, the usual intent-to-treat estimator is shown to be an upper bound on the postinfection causal vaccine effect provided that the magnitude of protection against infection is not too large. The methods are used to evaluate postinfection vaccine effects in a clinical trial of a rotavirus vaccine candidate and in a field study of a pertussis vaccine. Our results show that pertussis vaccination has a significant causal effect in reducing disease severity. [ABSTRACT FROM AUTHOR]

This article deals with the differential computation of sensitivity functions and confidence intervals for model output, when model inputs are subject to systematic or stochastic uncertainties with time-varying variances. The nonlinear, time-varying systems dealt with correspond to the class of nonlinear systems with time-invariant dynamics and boundary conditions involving algebraic-only equations. It is shown that the first-order kernel of a Volterra series expansion of the time-invariant model, allied with a derivation of the algebraic equations, can be used to derive approached differential formulas. These are applied to the case study of a real-size building thermal dynamic model developed with the Clim2000 software; the results are compared with Monte Carlo sampling and show very good agreement. [ABSTRACT FROM AUTHOR]

The analysis and comparison of a set of project alternatives implies balancing the different impact types so as to reach an evaluation of the merit of each alternative. Multicriteria analysis (MCA) proved to be useful for this kind of assessment by providing a framework to integrate the available information about the impacts with the values and preferences of stakeholders and decision-makers. This paper presents a case study in which MCA was applied to achieve a suitability ranking of the alternative land corridors that were proposed to host a road development within an alpine valley in northern Italy. [ABSTRACT FROM AUTHOR]

Before R. A. Fisher introduced randomized experimentation, the literature on empirical methods emphasized reducing heterogeneity of experimental units as the key to inference about the effects caused by treatments. To what extent is heterogeneity relevant to causal inference when ethical or practical constraints make random assignment infeasible? [ABSTRACT FROM AUTHOR]

This paper considers the stochastic capacitated arc routing problem (SCARP), obtained by taking random demands in the CARP. For real-world problems, it is important to create solutions that are insensitive to changes in demand, because these quantities are not deterministic but randomly distributed. This paper provides the basic concept of a new technique to compute such solutions, based upon the best method published for CARP: a hybrid genetic algorithm (HGA). The simulation analysis was achieved with the well-known DeArmon's, Eglese's and Belenguer's instances. This intensive evaluation process was carried out with 1000 replications providing high-quality statistical data. The results obtained prove that there is a great interest to optimize not only the solution cost but also the robustness of solutions. This work is a step forward to treat more realistic problems including industrial goals and constraints linked to demand variations. [ABSTRACT FROM AUTHOR]

In this paper, sensitivity analysis of the Charnes-Cooper-Rhodes model in data envelopment analysis (DEA) is studied for the case of perturbation of all outputs and of all inputs of an efficient decision-making unit (DMU). Using an approximate inverse of the perturbed optimal basis matrix, an approximate preservation of efficiency for an efficient DMU under these perturbations is considered. Sufficient conditions for an efficient DMU to preserve its efficiency are obtained in that case. An illustrative example is provided. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a modeling framework for deriving emissions corridors that preserve the Atlantic thermohaline circulation (THC). The framework consists of a multi-gas reduced-form climate model coupled to a four-box THC model and allows for the main physical uncertainties (i.e., climate and North Atlantic hydrological sensitivity) to be taken into account. The emissions corridors are calculated along the conceptual and methodological lines of the tolerable windows approach (TWA). The corridor boundaries demarcate emissions limits for the 21st century beyond which either the THC collapses or the mitigation burden becomes intolerable. Accordingly, the corridors represent the maneuvering space for climate policies committed to preserve the THC without endangering future economic growth. Results indicate a large dependence of the width of the emissions corridors on climate and North Atlantic hydrological sensitivity: for low values of climate and/or hydrological sensitivity the upper corridor boundary is far from being transgressed by any of the SRES emissions scenario for the 21st century, while for high values of both quantities even low non-intervention scenarios leave the corridor. [ABSTRACT FROM AUTHOR]

This article examines the potential for misleading inferences from conventional analyses and sensitivity analyses of observational data, and describes some proposed solutions based on specifying prior distributions for uncontrolled sources of bias. The issues are illustrated in a sensitivity analysis of confounding in a study of residential wire code and childhood leukemia and in a pooled analysis of 12 studies of magnetic-field measurements and childhood leukemia. Both analyses have been interpreted as evidence in favor of a causal effect of magnetic fields on leukemia risk. This interpretation is contrasted with results from analyses based on prior distributions for the unidentified bias parameters used in the original sensitivity-analysis model. These analyses indicate that accounting for uncontrolled confounding and response bias under a reasonable prior can substantially alter inferences about the existence of a magnetic-field effect. More generally, analyses with informative priors for unidentified bias parameters can help avoid misinterpretation of conventional results and ordinary sensitivity analyses. [ABSTRACT FROM AUTHOR]

A new form of matching—optimal balanced risk set matching—is applied in an observational study of a treatment, cystoscopy and hydrodistention, given in response to the symptoms of the chronic, nonlethal disease interstitial cystitis. When a patient receives the treatment at time t, that patient is matched to another patient with a similar history of symptoms up to time t who has not received the treatment up to time t; this is risk set matching. By using a penalty function in integer programming in a new way, we force the marginal distributions of symptoms to be balanced in the matched treated and control groups. Among all balanced matchings, we pick the one that is optimal in the sense of minimizing the multivariate pretreatment covariate distance within matched pairs. Under a simple model for the treatment assignment mechanism, we study the sensitivity of the findings to hidden biases. In particular, we show that a simple, conventional sensitivity analysis is appropriate with risk set matching when the time to treatment follows a proportional hazards model with a time-dependent unobserved covariate. [ABSTRACT FROM AUTHOR]

Alonso (1973, 1978) has proposed a generalized spatial interaction model from which the four members of the traditional family of gravity models (Wilson 1971) can be derived as special cases. With the aid of AIonso's general theory, this paper discusses the relationship between the traditional gravity models in greater detail and describes the performance of a series of what are termed quasi-constrained gravity models. The systemic parameters in Alonso's general model are discussed in terms of trip generation and trip attraction modeling. These systemic parameters are shown to reflect the relative importance of "site" and "situation" variables in determining outflow and inflow totals. When the site variables are accurate surrogates for actual outflow and inflow totals, a doubly constrained gravity model is likely to be optimal in terms of replicating existing flow patterns or forecasting unknown patterns. When the site variables are inaccurate surrogates and when outflow and inflow totals are related to situation variables, then lesser-constrained gravity models are likely to be optimal. Members of the traditional family of gravity models are shown to be the extreme points on a continuous surface of gravity models. [ABSTRACT FROM AUTHOR]

We describe a general approach using Bayesian analysis for the estimation of parameters in physiological pharmacokinetic models. The chief statistical difficulty in estimation with these models is that any physiological model that is even approximately realistic will have a large number of parameters, often comparable to the number of observations in a typical pharmacokinetic experiment (e. g., 28 measurements and 15 parameters for each subject). In addition, the parameters are generally poorly identified, akin to the well-known ill-conditioned problem of estimating a mixture of declining exponentials. Our modeling includes (a) hierarchical population modeling, which allows partial pooling of information among different experimental subjects, (b) a pharmacokinetic model including compartments for well-perfused tissues, poorly perfused tissues, fat, and the liver, and (c) informative prior distributions for population parameters, which is possible because the parameters represent real physiological variables. We discuss how to estimate the models using Bayesian posterior simulation, a method that automatically includes the uncertainty inherent in estimating such a large number of parameters. We also discuss how to check model fit and sensitivity to the prior distribution using posterior predictive simulation. We illustrate the application to the toxicokinetics of tetrachloroethylene (perchloroethylene [PERC]), the problem that motivated this work. [ABSTRACT FROM AUTHOR]

In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is one-half. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P-values, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this article we review and discuss the uses of Bayes factors in the context of five scientific applications in genetics, sports, ecology, sociology, and psychology. We emphasize the following points: • From Jeffreys' Bayesian viewpoint, the purpose of hypothesis testing is to evaluate the evidence in favor of a scientific theory. • Bayes factors offer a way of evaluating evidence in favor of a null hypothesis. • Bayes factors provide a way of incorporating external information into the evaluation of evidence about a hypothesis. • Bayes factors are very general and do not require alternative models to be nested. • Several techniques are available for computing Bayes factors, including asymptotic approximations that are easy to compute using the output from standard packages that maximize likelihoods. • In "nonstandard" statistical models that do not satisfy common regularity conditions, it can be technically simpler to calculate Bayes factors than to derive non-Bayesian significance tests. • The Schwarz criterion (or BIC) gives a rough approximation to the logarithm of the Bayes factor, which is easy to use and does not require evaluation of prior distributions. • When one is interested in estimation or prediction, Bayes factors may be converted to weights to be attached to various models so that a composite estimate or prediction may be obtained that takes account of structural or model uncertainty. • Algorithms have been proposed that allow model uncertainty to be taken into account when the class of models initially considered is very large. • Bayes factors are useful for guiding an evolutionary model-building process. • It is important, and feasible, to assess the sensitivity of conclusions to the prior distributions used. [ABSTRACT FROM AUTHOR]

In observational studies, subjects are not randomly assigned to treatment or control, so they may differ in their chances of receiving the treatment. A simple method is developed and demonstrated for displaying the sensitivity of conventional two-group permutation inferences to departures from random assignment of treatments. The unmatched case, discussed here, differs in certain technical and computational details from the matched case, discussed previously, however, the underlying model and the method for quantifying departures from randomization are the same. The method may be applied to Wilcoxon's rank sum test, the Gehan and log-rank tests for censored outcomes, Mantel's test for scored categories, and Fisher's exact test for binary responses. The method embeds the usual randomization reference distribution in a one-parameter family of departures involving an unobserved covariate that would have been controlled by adjustments had it been observed. As this parameter is varied, the sensitivity of permutational significance levels and confidence intervals is displayed. Brief discussion of an example shows that comparisons vary considerably in their degree of sensitivity to unobserved biases. [ABSTRACT FROM AUTHOR]

We study the problem of determining the location of an emergency transmitter in a downed aircraft. The observations are bearings read at fixed stations. A Bayesian approach, yielding a posterior map of probable locations, seems reasonable in this situation. We therefore develop conjugate prior distributions for the yon Mises distribution, which we use to compute a posterior distribution of the location. An approximation to the posterior distribution yields accurate, rapidly computable answers. A common problem with this kind of data is the possibility that signals will reflect off orographic terrain features, resulting in wild beatings. Such bearings can affect the posterior distribution severely. We develop a sensitivity analysis, based on the idea of predictive distribution, to reject wild beatings. The method, which is based on an asymptotic argument, nonetheless performs well in a small simulation study. When the preceding approximation is used, the sensitivity analysis is practical in terms of computation time. [ABSTRACT FROM AUTHOR]

The covariance matrix for the residuals of a regression process is written as the identity matrix plus a matrix V. The matrix V is bounded from above, and the corresponding set of generalized least squares estimates is identified. The extreme estimates in this set are functions of the usual t statistics; in particular the number ((T - k)/8)[sup 1/2]/|t| measures the influence of reweighting extreme observations, where T - k gives the degrees of freedom, t is the t statistic, and the weights on observations are allowed to vary by a factor of at most 2. [ABSTRACT FROM AUTHOR]

The process leading to partial classification with categorical data is sometimes nonrandom. A particular model accounting for incomplete data, which allows the probability of uncertain classification to depend on category identity, is utilized for an analysis of data obtained from a genetic study on Turner's syndrome. Estimates of population proportions are obtained from maximum likelihood. A method for handling nonrandomly missing data arrayed in contingency tables is discussed. Sensitivity analyses incorporating parameters related to the missing-data mechanism are recommended for estimation and testing. [ABSTRACT FROM AUTHOR]

Statistically matched files are created in an attempt to solve the practical problem that exists when no single file has the full set of variables needed for drawing important inferences. Previous methods of file matching are reviewed, and the method of file concatenation with adjusted weights and multiple imputations is described and illustrated on an artificial example. A major benefit of this approach is the ability to display sensitivity of inference to untestable assumptions being made when creating the matched file. [ABSTRACT FROM AUTHOR]

Visceral fat has long been associated with the development of insulin resistance. Although the mechanism is not well understood, it has been suggested that an increase in this fat depot results in an elevation in portal vein levels of free fatty acids and/or adipokines, adversely affecting hepatic glucose production. Overactivity of the endocannabinoid system is closely related to abdominal obesity and type 2 diabetes, suggesting CB1 receptor antagonism may exert its beneficial effects by decreasing visceral fat mass. A recent study published from our laboratory explores the role of chronic CB1 receptor antagonism and the longitudinal changes in insulin sensitivity and fat deposition in the canine model. [ABSTRACT FROM AUTHOR]