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Sample selection model is a solution to eliminate the nonresponse bias. In some applications nonresponse is a multilevel variable with respect to its reasons of occurring. In these cases, the sample selection model can be extended such that a model to be considered for each of the nonresponse reasons. Also, in many cases, the reasons for nonresponse have priority over each other. In other words, it is not possible to observe all of the nonresponse reasons simultaneously. For example, in a survey with two noncontact and refusal reasons, noncontact has priority over refusal and refusal can be observed if the contact to the respondent can be established. For analyzing such extended model, a Bayesian inference approach with multiple selection rules using multivariate normal, inverse gamma and LKJ distributions as prior distributions for parameters and possibility of priority for nonresponse reasons is presented. Simulation studies are performed and an establishment survey data set is analyzed to demonstrate the performance of the proposed method. For sensitivity analysis of nonresponse on the parameters of interest, posterior displacement is applied. [ABSTRACT FROM AUTHOR]

Many highly reliable products are designed to function for years without failure. For such systems accelerated degradation testing may provide significance information about the reliability properties of the system. In this paper, we propose the c-optimality criterion for obtaining optimal designs of constant-stress accelerated degradation tests where the degradation path follow a linear mixed effects model. The present work is mainly concerned with developing optimal designs in terms of the time variable rather than considering an optimal design of the stress variable which is usually considered in the majority of the literature. Finally, numerical examples are presented and sensitivity analysis procedures are conducted for evaluating the robustness of the optimal as well as standard designs against misspecifications of the nominal values. [ABSTRACT FROM AUTHOR]

Sensitivity analysis of random systems may convey important information on the dynamical properties of the system. In this paper, we determine the effects of parameters' perturbation on two dynamic poverty indexes: the headcount ratio and the income gap ratio. This is achieved by perturbing the generator of the Markov process governing the evolution in time of the economic agents among three classes of income, the initial distribution of individuals and the vector of mean income for poverty class. The paper presents two bounds on the aforementioned poverty indexes which show how the perturbations on the model parameters propagate on the poverty indexes. The paper contributes to the literature presenting for the first time effective bounds for dynamic poverty indicators and exploring the applicability of the perturbation approach to real data. [ABSTRACT FROM AUTHOR]

The local sensitivity analysis is recognized for its computational simplicity, and potential use in multi-dimensional and complex problems. Unfortunately, its major drawback is its asymptotic behavior where the prior to posterior convergence in terms of the standard metrics (and also computed by Fréchet derivative) used as a local sensitivity measure is not appropriate. The constructed local sensitivity measures do not converge to zero, and even diverge for the most multidimensional classes of prior distributions. Restricting the classes of priors or using other ϕ -divergence metrics have been proposed as the ways to resolve this issue which were not successful. We overcome this issue, by proposing a new flexible class of metrics so-called credible metrics whose asymptotic behavior is far more promising and no restrictions are required to impose. Using these metrics, the stability of Bayesian inference to the structure of the prior distribution will be then investigated. Under appropriate condition, we present a uniform bound in a sense that a close credible metric a priori will give a close credible metric a posteriori. As a result, we do not get the sort of divergence based on other metrics. We finally show that the posterior predictive distributions are more stable and robust. [ABSTRACT FROM AUTHOR]

This article considers a multi-period weighted mean-variance portfolio selection problem with uncertain time-horizon and a stochastic cash flow in a Markov regime-switching market. The random returns of risky assets and amount of the cash flow all depend on the states of a stochastic market which are assumed to follow a discrete-time Markov chain. Based on the conditional distribution of uncertain time-horizon caused by exogenous factors, we construct a more general mean-variance investment model. Within a game theoretic framework, we derive the equilibrium strategy and equilibrium value function in closed-form by applying backward induction approach. In addition, we show the equilibrium efficient frontier and discuss some degenerate cases. Finally, some numerical examples and sensitivity analysis are presented to illustrate equilibrium efficient frontiers and the effects of uncertain time-horizon on the equilibrium strategy and equilibrium efficient frontier as well as regime-switching and stochastic cash flow on the equilibrium efficient frontier. [ABSTRACT FROM AUTHOR]

In this study, a quasi-renewal process model with δ-shock is analyzed for a degenerative repairable supply chain with a single supplier source. The supplier of the supply chain system encounters shocks due to crisis events and, as a consequence, tends to fail if the interarrival time between two successive shocks is less than a random threshold. The non negligible repair time of the supplier and the random threshold time of shocks are modeled on a quasi renewal process while the shock arrivals are governed by a renewal process. The long-run average cost per unit time is derived by employing the reengineering strategy N, which is determined by the number of failures of the supplier. The optimal reengineering policy N ∗ is obtained analytically by minimizing the long-run expected cost rate. Numerical illustrations and sensitivity analysis evidence the consideration of maintenance policy for a repairable supply chain to sustain its smooth running. [ABSTRACT FROM AUTHOR]

In this article, we consider a single server queuing model with correlated reneging and feedback of served customers. The steady-state solution of the model is obtained using the matrix-decomposition method. The transient analysis of the model is also performed with the help of the Runge-Kutta method. Important measures of performance like expected queue length and expected waiting time in the queue are computed. Some comparisons are performed, and finally, the sensitivity analysis of the model is also carried out. [ABSTRACT FROM AUTHOR]

Missing data is a common problem in general applied studies, and specially in clinical trials. For implementing sensitivity analysis, several multiple imputation methods exist, like sequential imputation, which restricts to monotone missingness, and Bayesian, where the imputation and analysis models differ, entailing overestimation of variance. Also, full conditional specification provides a conditional interpretation of sensitivity parameters, requiring further calibration to get the desired marginal interpretation. We propose in this paper a multiple imputation procedure, based on a multivariate linear regression model, which keeps compatibility in sensitivity analysis under intermittent missingness, providing a marginal interpretation of the elicited parameters. Simulation studies show that the method behaves well with longitudinal data and remains robust under demanding constraints. We conclude the possibility of situations not covered by the existing methods and well suited for our proposal, which allows more efficient handling of a given multivariate linear regression structure. Its use is illustrated in a real case study, where a sensitivity analysis is accomplished. [ABSTRACT FROM AUTHOR]

In this paper, we consider estimation and inference procedures in spatial linear models when some of the covariates are measured with errors. It is assumed that the additive error distributed according to the law belonging to the class of elliptically contoured distributions. The development of the corrected score function with the family of elliptical distributions is the basis for derivation of the estimators. For sensitivity analysis, the local influence approach is used for assessing influence of small perturbations on the parameter estimates. A simulation study is presented illustrating the good performance of the proposed approach, including the robustness property for the heavier tail models. We further illustrate the proposed procedures by an application in a real dataset. [ABSTRACT FROM AUTHOR]

This paper investigates the reliability and sensitivity analysis for a repairable k-out-of-n:G system with retrial of failed components. Such a model has important practical applications in fully automatic systems, and the most typical one is fully automatic manufacturing system. Markov models for availability and reliability of the system whose components are all subject to two failure modes are presented. There is no waiting space for failed components in the system. If a failed component finds the repairman busy and it can not be repaired at once, it will enter into the retrial orbit and try again for repair after some random period of time. Some reliability indexes, including steady-state availability, reliability function and mean time to system first failure, are derived by using vector Markov process and Laplace transform theory. Sensitivity analysis and relative sensitivity analysis are provided as well. Finally, some numerical experiments are conducted to show the effects of system parameters on the system reliability indexes. [ABSTRACT FROM AUTHOR]

The Bayesian network is crucial for computer technology and artificial intelligence when dealing with probabilities. In this paper, we extended a new semiparametric model for Bayesian networks which is more flexible and robust than the parametric or linear one, providing a further generalization of the Gaussian Bayesian network. In the classical Gaussian Bayesian networks, the regression function between nodes has always been assumed to be linear. Actually, this is not necessary because the links between nodes may be more complex than simply linear relationships. Learning the structure of the semiparametric Bayesian network, by adding the nonlinear structures, was an important issue discussed in this work. We have illustrated the problem of estimating and testing both parameters and regression functions of the proposed model. We, then, introduced a new algorithm for constructing the proposed semiparametric Bayesian network. Some sensitivity analyses have been explained in order to validate the correctness of the network. Simulation studies and a real application for the energy field were used to examine the fitted model. [ABSTRACT FROM AUTHOR]

In this paper, a synthetic control chart is proposed by integrating the salient features of the npx chart and the CRL chart. The synthetic chart achieves higher detection effectiveness on both small and large mean shifts while retaining the operational simplicity of the attribute charts owing to only using attribute inspection. Both statistical and economic design of the synthetic chart are considered and numerical tests have indicated that the synthetic chart has a higher power for detecting mean shifts than the npx chart, MON chart and CUSUM chart. In addition, sensitivity analyses are also performed under both the statistical and economic design model. [ABSTRACT FROM AUTHOR]

An economic-statistical design of the synthetic double sampling (synDS) T2 chart is presented in this study. The cost function is minimized to obtain the optimal design parameters of the synDS T2 chart by incorporating the statistical constraints or the constraints on the average number of samples. An example is provided and a sensitivity analysis is conducted to study the effect of model parameters on the optimal solution of the design. The numerical comparison shows that the synDS T2 chart performs better than the synthetic T2 chart and the multivariate exponentially weighted moving average chart, in terms of the cost. [ABSTRACT FROM AUTHOR]

This paper studies an M/G/1 clearing queueing system with setup time and multiple vacations, in which all present customers in the system are served simultaneously and breakdowns may occur in busy or setup period. We investigate the stationary distribution of system size and the Laplace–Stieltjes transform of sojourn time. In addition, various performance measures are discussed, such as the mean system size at arbitrary time and the mean length of a vacation circle. Moreover, a cost analysis is carried out for this queueing system. Numerical results are presented to study the sensitivity of the system parameters on the expected cost function and system performances. [ABSTRACT FROM AUTHOR]

The economic design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects, one of the proper alternatives may be the Burr XII (Burr) distribution which is quite flexible and its hazard rate can be fixed, increasing, decreasing, single mode or even U-shaped. Yet, in Burr distribution context, only one assignable cause model were considered where more realistic approach may be to consider multiple assignable causes. Thus, in this paper, an economic design of the control charts under the Burr shock model and multiple assignable causes were considered and compared with three types of prior distribution for the mean shift parameter. Also, a numerical example and a sensitivity analysis for more illustration and studying the effect of changing parameters of the shock model distribution on the optimum values of proposed design models were conducted, respectively. The results indicate that when the prior distribution is of negative exponential distribution, the multiple assignable cause models under Burr XII (Burr) distribution have minimum loss-cost. [ABSTRACT FROM AUTHOR]

The purpose of this article is to present a new policy for designing an acceptance sampling plan based on the minimum proportion of the lot that should be inspected in the presence of inspection errors. It is assumed that inspection is not perfect and every defective item cannot be detected with complete certainty. The Bayesian method is used for obtaining the probability distribution function of the number of defective items in the lot. To design this model, two constraints of producer risk and consumer risk are considered during the inspection process by using two specified points on operating characteristic curve. In order to illustrate the application of the proposed model, an example is presented. In addition, a sensitivity analysis is performed to analyze the model performance under different scenarios of process parameters and the results are elaborated. Finally, the efficiency of the proposed model is compared with the sampling method of Spencer and Kevan de Lopez (2017) at the same conditions. [ABSTRACT FROM AUTHOR]

Sihm et al. (2016) proposed an unrelated question binary optional randomized response technique (RRT) model for estimating the proportion of population that possess a sensitive characteristic and the sensitivity level of the question. In our work, decision theoretic approach has been followed to obtain Bayes estimates of the two parameters along with their corresponding minimal Bayes posterior expected losses (BPEL) using beta prior and squared error loss function (SELF). Relative losses are also examined to compare the performances of the Bayes estimates with those of the classical estimates obtained by Sihm et al. (2016). The results obtained are illustrated with the help of real survey data using non informative prior. [ABSTRACT FROM AUTHOR]

In this article, we evaluate the relative cost of a targeted design to an untargeted design for a randomized clinical trial comparing a new treatment to a control. We observed that when the ratio of screen cost per person and drug cost per person is very small, say 0.008, the trial cost for conducting a targeted design might be fewer than that for conducting an untargeted design. When the difference between the treatment effect for the targeted patients and the treatment effect for the untargeted patients is increased, the savings in trial cost for the targeted design would be increased significantly. The greater sensitivity and specificity for the screen tool will lead to greater savings in trial cost for the targeted design. The relative cost of a targeted design to an untargeted design is associated with the treatment effect difference between targeted and untargeted patients, the proportion of targeted patients in the population, the screen tool performance, and the ratio of drug cost per person and screen cost per person. [ABSTRACT FROM PUBLISHER]

New statistical techniques and procedures have been developed to control high-yield processes along with looking for process improvement opportunities and minimizing production cost. Cumulative count of conforming control chart is generally a technique for high-quality processes, when nonconforming items are rarely produced. The objective of this study is to design control chart based on cumulative count of conforming items and run rules that develops an economic model based on the average number of inspected items to design m-of-mCCCchart in order to facilitate minimum average cost per item produced. The optimal design parameters for different values of nonconforming fraction and different cost parameters in each scenario are determined. Finally, to analyze the behavior of optimal economic solutions, sensitivity analysis of the model parameters is performed. [ABSTRACT FROM PUBLISHER]

Runs rules are commonly used to increase the sensitivity of the Shewhartchart, for detecting small and moderate mean shifts. This paper presents a group runs (GR) revisedm-of-k(R−m/k) runs rule chart for the mean. The proposed GRR−m/kruns rule chart has superior zero-state and steady-state performances to the basic GR, synthetic andR−m/kruns rule charts, for detecting small and moderate shifts, while maintaining the same efficiency toward large shifts. For zero-state case, the proposed chart can be designed to outperform the exponentially weighted moving average (EWMA) chart, except for small shifts. However, the EWMA chart prevails for steady-state case. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sampling policy considering Bayesian risks. Various definitions of producer's risk and consumer's risk have been made. Bayesian risks for both producer and consumer are proven to give better information to decision-makers than classical definitions of the risks. So considering the Bayesian risk constraints, we seek to find optimal acceptance sampling policy by minimizing total cost, including the cost of rejecting the batch, the cost of inspection, and the cost of defective items detected during the operation. Proper distributions to construct the objective function of the model are specified. In order to demonstrate the application of the proposed model, we illustrate a numerical example. Furthermore, the results of the sensitivity analysis show that lot size, the cost of inspection, and the cost of one defective item are key factors in sampling policies. The acceptable quality level, the lot tolerance proportion defective, and Bayesian risks also affect the sampling policy, but variations of acceptable quality level and producer Bayesian risks, for values more than a specified value, cause no changes in sampling policy. [ABSTRACT FROM PUBLISHER]

Measurement error is well known to cause bias in estimated regression coefficients and a loss of power for detecting associations. Methods commonly used to correct for bias often require auxiliary data. We develop a solution for investigating associations between the change in an imprecisely measured outcome and precisely measured predictors, adjusting for the baseline value of the outcome when auxiliary data are not available. We require the specification of ranges for the reliability or the measurement error variance. The solution allows one to investigate the associations for change and to assess the impact of the measurement error. [ABSTRACT FROM AUTHOR]

We develop new Bayesian regression tests for prespecified regression coefficients. Simple, closed forms of the Bayes factors are derived that depend only on the regressiont-statistic andF-statistic and the usual associatedtandFdistributions. The priors that allow those forms are simple and also meaningful, requiring minimal but practically important subjective inputs. [ABSTRACT FROM AUTHOR]

In situations where 6σ is much smaller than the spread in specifications, the conventional p-chart is not applicable for monitoring a lower fraction non conforming process. In 6σ processes, a drift of the process mean is often allowed within at most 1.5σ from the target, and a modified control chart is a useful alternative to the usualchart. This paper proposes aZPcontrol chart as a more efficient alternative to the conventional p-chart, and a simultaneous control scheme for mean and fraction non conforming in small process variance is discussed. [ABSTRACT FROM AUTHOR]

In a model of the formY=h(X1, …,Xd) where the goal is to estimate a parameter of the probability distribution ofY, we define new sensitivity indices which quantify the importance of each variableXiwith respect to this parameter of interest. The aim of this paper is to definegoal oriented sensitivity indicesand we will show that Sobol indices are sensitivity indices associated to a particular characteristic of the distributionY. We name the framework we present asGoal Oriented Sensitivity Analysis(GOSA). [ABSTRACT FROM AUTHOR]

Runs rules are usually used with Shewhart-type charts to enhance the charts' sensitivities toward small and moderate shifts. Abbas et al. in 2011 took it a step further by proposing two runs rules schemes, applied to the exponentially weighted moving average (EWMA) chart and evaluated their average run length (ARL) performances using simulation. They showed that the proposed schemes are superior to the classical EWMA chart and other schemes being investigated. Besides pointing out some erroneous ARL and standard deviation of the run length (SDRL) computations in Abbas et al., this paper presents a Markov chain approach for computing the ARL, percentiles of the run length (RL) distribution and SDRL, for the two runs rules schemes of Abbas et al. Using Markov chain, we also propose two combined runs rules EWMA schemes to quicken the two schemes of Abbas et al. in responding to large shifts. The runs rules (basic and combined rules) EWMA schemes will be compared with some existing control charting methods, where the former charts are shown to prevail. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the best linear unbiased estimators (BLUEs) based on double ranked set sampling (DRSS) and ordered DRSS (ODRSS) schemes for the simple linear regression model with replicated observations. We assume three symmetric distributions for the random error term, i.e., normal, Laplace and some scale contaminated normal distributions. The proposed BLUEs under DRSS (BLUEs-DRSS) and ODRSS (BLUEs-ODRSS) are compared with the BLUEs based on ordered simple random sampling (OSRS), ranked set sampling (RSS), and ordered RSS (ORSS) schemes. These estimators are compared in terms of relative efficiency (RE), RE of determinant (RED), and RE of trace (RET). It is found that the BLUEs-ODRSS are uniformly better than the BLUEs based on OSRS, RSS, ORSS, and DRSS schemes. We also compare the estimators based on imperfect RSS (IRSS) schemes. It is worth mentioning here that the BLUEs under ordered imperfect DRSS (OIDRSS) are better than their counterparts based on IRSS, ordered IRSS (OIRSS), and imperfect DRSS (IDRSS) methods. Moreover, for sensitivity analysis of the BLUEs, we calculate REs and REDs of the BLUEs under the assumption of normality when in fact the parent distribution follows a non normal symmetric distribution. It turns out that even under violation of normality assumptions, BLUEs of the intercept and the slope parameters are found to be unbiased with equal REs under each sampling scheme. It is also observed that the BLUEs under ODRSS are more efficient than the existing BLUEs. [ABSTRACT FROM AUTHOR]

Various methods have been proposed to estimate intra-cluster correlation coefficients (ICCs) for correlated binary data, and many are very sensitive to the type of design and underlying distributional assumptions. We proposed a new method to estimate ICC and its 95% confidence intervals based on resampling principles and U-statistics, where we resampled with replacement pairs of individuals from within and between clusters. We concluded from our simulation study that the resampling-based estimates approximate the population ICC more precisely than the analysis of variance and method of moments techniques for different event rates, varying number of clusters, and cluster sizes. [ABSTRACT FROM PUBLISHER]

This article focuses on reducing the additional variance due to randomization of the responses. The idea of additive scrambling and its inverse has been used along with (i) split sample approach and (ii) double response approach. Specifically, our proposal is based on Gupta et al. (2006) randomized response model. We selected this model for improvement because it provides estimator of mean and sensitivity level of a sensitive variable and is better than all of its competitors proposed earlier to it and even Gupta et al. (2006) sensitivity estimator is better than that of Gupta et al. (2010). Our suggested estimators are unbiased estimators and perform better than Gupta et al. (2006) estimator. The issue of privacy protection is also discussed. [ABSTRACT FROM PUBLISHER]

We formulate a new cure rate survival model by assuming that the number of competing causes of the event of interest has the Poisson distribution, and the time to this event has the generalized linear failure rate distribution. A new distribution to analyze lifetime data is defined from the proposed cure rate model, and its quantile function as well as a general expansion for the moments is derived. We estimate the parameters of the model with cure rate in the presence of covariates for censored observations using maximum likelihood and derive the observed information matrix. We obtain the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform global influence analysis. The usefulness of the proposed cure rate survival model is illustrated in an application to real data. [ABSTRACT FROM PUBLISHER]

The steady-state average run length (ARL) is a function of the in-control probabilities of being in each nonabsorbing state. Davis and Woodall (2002) tabulated values that are significantly smaller than the steady-state ARLs, because they used the out-of-control probabilities. The synthetic chart signals when a second sample point falls beyond the control limits, no matter whether one of them falls above the centerline and the other falls below it. The side-sensitive version of the synthetic chart does not signal when the points beyond the control limits are on opposite sides. With this rule, the chart detects mean changes more quickly. [ABSTRACT FROM AUTHOR]

Regression models with random effects are proposed for joint analysis of negative binomial and ordinal longitudinal data with nonignorable missing values under fully parametric framework. The presented model simultaneously considers a multivariate probit regression model for the missing mechanisms, which provides the ability of examining the missing data assumptions and a multivariate mixed model for the responses. Random effects are used to take into account the correlation between longitudinal responses of the same individual. A full likelihood-based approach that allows yielding maximum likelihood estimates of the model parameters is used. The model is applied to a medical data, obtained from an observational study on women, where the correlated responses are the ordinal response of osteoporosis of the spine and negative binomial response is the number of joint damage. A sensitivity of the results to the assumptions is also investigated. The effect of some covariates on all responses are investigated simultaneously. [ABSTRACT FROM AUTHOR]

Long-memory tests are often complicated by the presence of deterministic trends. Hence, an additional step of detrending the data is necessary. The typical way to detrend a suspected long-memory series is to use OLS or BSP residuals. Applying the method of sensitivity analysis we address the of question of how robust these residuals are in presence of potential long memory components. Unlike short-memory ARMA process long-memory I(d) processes causes sensitivity to OLS/BSP residuals. Therefore, we develop a finite sample measure of the sensitivity of a detrended series based on the residuals. Based on our sensitivity measure we propose a “rule of thumb” for practitioners to choose between the two methods of detrending, has been provided in this article. [ABSTRACT FROM AUTHOR]

In an acceptance-sampling plan, where items of an incoming batch of products are inspected one by one, if the number of conforming items between successive non conforming items falls below a lower control threshold, the batch is rejected. If it falls above an upper control threshold, the batch is accepted, and if it lies within the thresholds then the process of inspecting the items continues. The purpose of this article is to develop an optimization model to determine the optimum values of the thresholds such that constraints on the probability of Type I and Type II errors are satisfied. This article starts by developing a Markovian model to derive the expected total cost of the inspection problem containing the costs of acceptance, rejection, and inspection. Then, the optimum values of the thresholds are selected in order to minimize the expected cost. To demonstrate the application of the proposed methodology, perform sensitivity analysis, and compare the performance of the proposed procedure to the one of another method, a numerical example is given at the end and the results are reported. [ABSTRACT FROM AUTHOR]

For regression analysis of data with non response, sensitivity analysis is usually recommended. An index of local sensitivity to non ignorability (ISNI) (Troxel et al., 2004) was derived to detect the sensitivity of maximum likelihood estimates to small departures from ignorability. However, ISNI requires specification of a parametric model for the missing-data mechanism. In this article, a local sensitivity index for a pseudolikelihood (PL) method that does not require specification of the mechanism is proposed. For bivariate data (x,y), when the non response mechanism is an arbitrary function ofx + λy, this new index is defined as the first derivative of the PL estimate with respect to λ at λ = 0. The closed form was derived for normal regression data when the density function of the predictorxapproximated by a kernel estimator in the PL method. The utility of this new local sensitivity index was illustrated through application on one dataset. [ABSTRACT FROM PUBLISHER]

In this article, we focus on the general k-step step-stress accelerated life tests with Type-1 censoring for two-parameter Weibull distributions based on the tampered failure rate (TFR) model. We get the optimum design for the tests under the criterion of the minimization of the asymptotic variance of the maximum likelihood estimate of the pth percentile of the lifetime under the normal operating conditions. Optimum test plans for the simple step-stress accelerated life tests under Type-I censoring are developed for the Weibull distribution and the exponential distribution in particular. Finally, an example is provided to illustrate the proposed design and a sensitivity analysis is conducted to investigate the robustness of the design. [ABSTRACT FROM AUTHOR]

In randomized trials, investigators are frequently interested in estimating the direct effect of a treatment on an outcome that is not relayed by intermediate variables, in addition to the usual intention-to-treat (ITT) effect. Even if the ITT effect is not confounded due to randomization, the direct effect is not identified when unmeasured variables affect the intermediate and outcome variables. Although the unmeasured variables cannot be adjusted for in the models, it is still important to evaluate the potential bias of these variables quantitatively. This article proposes a sensitivity analysis method for controlled direct effects using a marginal structural model that is an extension of the sensitivity analysis method of unmeasured confounding introduced in the context of observational studies. The proposed method is illustrated using a randomized trial of depression. [ABSTRACT FROM AUTHOR]

The problem of choosing the loss function in the Bayesian problem of many hypotheses testing is considered. It is shown that linear and quadratic loss functions are the most-used ones. For any kind of loss function, the risk function in the Bayesian problem of many hypotheses testing contains the errors of two kinds. The Bayesian decision rule minimizes the total effect of these errors. The share of each of them in the optimal value of risk function is unknown. When solving many important problems, the results caused by different errors significantly differ from each other. Therefore, it is necessary to guarantee the limitation on the most undesirable kind of these errors and to minimize the errors of the second kind. For solving these problems, this article are states and solves conditional Bayesian tasks of testing many hypotheses. The results of sensitivity analysis of the classical and conditional Bayesian problems are given and their advantages and drawbacks are considered. [ABSTRACT FROM AUTHOR]

Pool-testing is a cost effective method over individual testing. When imperfect tests are used in pool testing strategy, there tend to be loss of sensitivity. The loss in sensitivity can be recovered by re-testing the pools classified initially. Apart from improving the sensitivity of the testing scheme, it also improves the efficiency of the estimator. This article proposes a pool-testing scheme with re-testing, and estimate the prevalence based on the proposed testing scheme. [ABSTRACT FROM AUTHOR]

A new procedure is introduced for conducting screening experiments to find a small number of influential factors from among a large number of factors with negligible effects. It is intended for experiments in which the factors are easily controlled, as in simulation models. It adds observations sequentially after conducting a small initial experiment. The performance of the procedure is investigated using simulation, and evidence is presented that this and other procedures scale as the logarithm of the total number of factors if the number of influential factors is fixed. An investigation of the new procedure for 1-3 active factors shows that it compares favorably with competing methods, particularly when the size of the nonzero effects is 1-2 times the standard deviation. A limited look at the procedure for up to 6 active factors is also presented. [ABSTRACT FROM AUTHOR]

Unmeasured confounding poses a common problem in observational studies. Although sensitivity analysis based on marginal structural models can now be used in the sensitivity analysis of the causal risk difference, this method cannot be simply applied to the causal risk ratio. Here, we propose a method of sensitivity analysis for the causal risk ratio that extends previous work on the causal risk difference, and apply it to a classic cohort study investigating behavior patterns in coronary heart disease. We also extend the proposed method to a sensitivity analysis of the causal risk ratio of a time-varying treatment on repeated outcomes. The proposed method helps to provide a realistic picture of the potential impact of unmeasured confounding. [ABSTRACT FROM AUTHOR]

This article develops a method for computing the sensitivity analysis in a Gaussian Bayesian network. The measure presented is based on the Kullback-Leibler divergence and is useful to evaluate the impact of prior changes over the posterior marginal density of the target variable in the network. We find that some changes do not disturb the posterior marginal density of interest. Finally, we describe a method to compare different sensitivity measures obtained depending on where the inaccuracy was. An example is used to illustrate the concepts and methods presented. [ABSTRACT FROM AUTHOR]

In terms of the relationship between the lower confidence limits of reliabilities for system and component, using the finite Markov chain imbedding approach, we analyze the sensitivity of reliability for a linear consecutive-k-out-of-n:F system, a circular consecutive-k-out-of-n:F system, and a k-within-consecutive-r-out-of-n:F system. The analysis is based on two parameters: the number of experiments and the number of failed components. [ABSTRACT FROM AUTHOR]