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With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center point of its rotated bounding box. The proposed orientation-aware center point estimation network uses deep layer aggregation network to search center points and regress to all other damage properties, such as size and angle. A loss function is designed to improve the optimization efficiency of network. Orientation-aware center point estimation network is applied to bolt damage detection, and comparison with the well-known Faster Region-Convolutional Neural Network (a benchmark using horizontal bounding box) demonstrates the accuracy of the proposed method. Finally, videos were utilized to verify the capability of the proposed orientation-aware center point estimation network in real-time detection of bolt damages.

A two-sample pivot for comparing the means from independent populations is well known. For large sample sizes, the distribution of the pivot is routinely approximated by a standard normal distribution. The question is about the thinking process that may guide one to rationalize invoking the asymptotic theory. In this pedagogical piece, we put forward soft statistical arguments to make users feel more at ease by suitably indexing the sample sizes from a practical standpoint that would allow a valid interpretation and understanding of pointwise convergence of the pivot's cumulative distribution function (c.d.f.) to the c.d.f. of a standard normal random variable.

Expert-coded datasets provide scholars with otherwise unavailable cross-national longitudinal data on important concepts. However, expert coders vary in their reliability and scale perception, potentially resulting in substantial measurement error; this variation may correlate with outcomes of interest, biasing results in analyses that use these data. This latter concern is particularly acute for key concepts in peace research. In this article, I describe potential sources of expert error, focusing on the measurement of identity-based discrimination. I then use expert-coded data on identity-based discrimination to examine: 1) the implications of measurement error for quantitative analyses that use expert-coded data, and 2) the degree to which different techniques for aggregating these data ameliorate these issues. To do so, I simulate data with different forms and levels of expert error and regress conflict onset on different aggregations of these data. These analyses yield two important results. First, almost all aggregations show a positive relationship between identity-based discrimination and conflict onset consistently across simulations, in line with the assumed true relationship between the concept and outcome. Second, different aggregation techniques vary in their substantive robustness beyond directionality. A structural equation model provides the most consistently robust estimates, while both the point estimates from an Item Response Theory (IRT) model and the average over expert codings provide similar and relatively robust estimates in most simulations. The median over expert codings and a naive multiple imputation technique yield the least robust estimates.

The mixture cure model has been widely applied to survival data in which a fraction of the observations never experience the event of interest, despite long-term follow-up. In this paper, we study the Cox proportional hazards mixture cure model where the covariate effects on the distribution of uncured subjects’ failure time may jump when a covariate exceeds a change point. The nonparametric maximum likelihood estimation is used to obtain the semiparametric estimates. We employ a two-step computational procedure involving the Expectation-Maximization algorithm to implement the estimation. The consistency, convergence rate and asymptotic distributions of the estimators are carefully established under technical conditions and we show that the change point estimator is n consistency. The m out of n bootstrap and the Louis algorithm are used to obtain the standard errors of the estimated change point and other regression parameter estimates, respectively. We also contribute a test procedure to check the existence of the change point. The finite sample performance of the proposed method is demonstrated via simulation studies and real data examples.

It is fairly common to rank different geographic units, e.g. counties in the USA, based on health indices. In a typical application, point estimates of the health indices are obtained for each county, and the indices are then simply ranked as if they were known constants. Several authors have considered optimal rank estimators under squared error loss on the rank scale as a default method for general purpose ranking, e.g. situations where ranking units across the full spectrum of performance (low, medium, high) is important. While computationally convenient, squared error loss on the rank scale may not represent the true inferential goals of rank consumers. We construct alternative loss functions based on three components: (1) the inferential goal (rank position or pairwise comparisons), (2) the scale (original, log-transformed or rank) and (3) the (positional or pairwise) loss function (0/1, squared error or absolute error). We can obtain optimal ranks for loss functions based on rank positions and nearly optimal ranks for loss functions based on pairwise comparisons paired with highest posterior density (HPD) credible intervals. We compare inferences produced by the various ranking methods, both optimal and heuristic, using low birth weight data for counties in the Midwestern United States, from 2006 to 2012.

To increase power or reduce the number of patients needed in trials studying treatments for psychiatric or mental disorders with a high placebo response rate, we may consider use of the sequential parallel comparison design proposed elsewhere. Because statistical significance does not necessarily imply that the difference between treatment and placebo is of clinical importance, it is always of importance to quantify the treatment effect in clinical trials. When the patient responses are dichotomous, the treatment and other covariates effects are not likely additive. Thus, using a weighted average of the risk differences over two phases may not be a meaningful summary index to measure the treatment effect. To alleviate this concern, we consider use of the relative difference or relative risk reduction to measure the treatment effect. We derive both point and interval estimators for the relative difference by use of the weighted-least-squares estimator and Mantel-Haenszel type estimator. We employ Monte Carlo simulation to evaluate the finite-sample performance of these estimators in a variety of situations. We also include a procedure for testing the homogeneity of the relative difference between phases under the sequential parallel comparison design. We use the placebo-controlled study to assess the efficacy of a low dose of aripiprazole adjunctive to antidepressant therapy in the treatment of patients with major depressive disorder to illustrate the use of estimators developed here.

In this article, we present the wald_mse command, which computes the maximum mean squared error of a user-specified point estimator of the mean for a population of interest in the presence of missing data. As pointed out by Manski (1989, Journal of Human Resources 24: 343–360; 2007, Journal of Econometrics 139: 105–115), the presence of missing data results in the loss of point identification of the mean unless one is willing to make strong assumptions about the nature of the missing data. Despite this, decision makers may be interested in reporting a single number as their estimate of the mean as opposed to an estimate of the identified set. It is not obvious which estimator of the mean is best suited to this task, and there may not exist a universally best choice in all settings. To evaluate the performance of a given point estimator of the mean, wald_mse allows the decision maker to compute the maximum mean squared error of an arbitrary estimator under a flexible specification of the missing-data process.

Because of recent failures in the past, the role of Board and the Board monitoring have become important. The directors are expected to be more accountable. In this study, the researcher tries to investigate the relationship between the level of board activity and firm value for firms in FMCG sector over a three-year period from 2010–2011 to 2012–2013. The primary aim of the article is to provide empirical evidence and specifically find out the impact of board activity measured by number of meetings and its impact on firm performance. The results indicate that the attendance in board meetings is significantly positively correlated with ROA. The OLS results with both the performance measures show that the point estimates of attendance at board meetings were significant indicating that attendance in such meetings is perceived to be an indicator of good monitoring activities of the board.

Background. The National Institute for Quality and Efficiency in Health Care (IQWiG) employs an efficiency frontier (EF) framework to facilitate setting maximum reimbursable prices for new interventions. Probabilistic sensitivity analysis (PSA) is used when yes/no reimbursement decisions are sought based on a fixed threshold. In the IQWiG framework, an additional layer of complexity arises as the EF itself may vary its shape in each PSA iteration, and thus the willingness-to-pay, indicated by the EF segments, may vary. Objectives. To explore the practical problems arising when, within the EF approach, maximum reimbursable prices for new interventions are sought through PSA. Methods. When the EF is varied in a PSA, cost recommendations for new interventions may be determined by the mean or the median of the distances between each intervention’s point estimate and each EF. Implications of using these metrics were explored in a simulation study based on the model used by IQWiG to assess the cost-effectiveness of 4 antidepressants. Results. Depending on the metric used, cost recommendations can be contradictory. Recommendations based on the mean can also be inconsistent. Results (median) suggested that costs of duloxetine, venlafaxine, mirtazapine, and bupropion should be decreased by €131, €29, €12, and €99, respectively. These recommendations were implemented and the analysis repeated. New results suggested keeping the costs as they were. The percentage of acceptable PSA outcomes increased 41% on average, and the uncertainty associated to the net health benefit was significantly reduced. Conclusions. The median of the distances between every intervention outcome and every EF is a good proxy for the cost recommendation that would be given should the EF be fixed. Adjusting costs according to the median increased the probability of acceptance and reduced the uncertainty around the net health benefit distribution, resulting in a reduced uncertainty for decision makers.

Simon’s two-stage design has been widely used in early phase clinical trials to assess the activity of a new investigated treatment. In practice, the actual sample sizes do not always follow the study design precisely, especially in the second stage. When over- or under-enrollment occurs in a study, the original critical values for the study design are no longer valid for making proper statistical inference in a clinical trial. The hypothesis for such studies is always one-sided, and the null hypothesis is rejected when only a few responses are observed. Therefore, a one-sided lower interval is suitable to test the hypothesis. The commonly used approaches for confidence interval construction are based on asymptotic approaches. These approaches generally do not guarantee the coverage probability. For this reason, Clopper-Pearson approach can be used to compute exact confidence intervals. This approach has to be used in conjunction with a method to order the sample space. The frequently used method is based on point estimates for the response rate, but this ordering has too many ties which lead to conservativeness of the exact intervals. We propose developing exact one-sided intervals based on the p-value to order the sample space. The proposed approach outperforms the existing asymptotic and exact approaches. Therefore, it is recommended for use in practice.

In clinical neuropsychology, it is often necessary to estimate a patient’s premorbid level of cognitive functioning in order to evaluate whether his scores on cognitive tests should be considered abnormal. In practice, test results from before the onset of brain pathology are rarely available, and the patient’s level of education is used instead as an estimate of his premorbid level. Unfortunately, level of education may be expressed on many different scales of education, which are difficult to use interchangeably. Here, we introduce a new scale that has the capacity to replace existing scales and can be used interchangeably with any of them: the Universal Scale of Intelligence Estimates (USIE). To achieve this, we propose to map all levels of existing educational scales to standard IQ scores. This USIE point estimate is supplemented with an estimation interval. We assert that USIE offers some important benefits for clinical practice and research.

This analysis draws on binational data from an ethnosurvey conducted in Guatemala and in the United States in Providence, Rhode Island, to develop a refinement of the weighting scheme that the Mexican Migration Project (MMP) uses. The alternative weighting procedure distinguishes between temporary and settled migrants by using a question on household location in the Guatemala questionnaire that is not used in the MMP. Demographic characteristics and integration experiences of the most recent U.S. trip are used to assess the composition and representativeness of the U.S. sample. Using a composite index of migrant integration to compare the impact of alternative U.S. sample weights on point estimates, I find that although the U.S. sample is broadly representative across a range of background characteristics, the MMP sample weighting procedure biases estimates of migrant integration downward.

We introduce the rdlocrand package, which contains four commands to conduct finite-sample inference in regression discontinuity (RD) designs under a local randomization assumption, following the framework and methods proposed in Cattaneo, Frandsen, and Titiunik (2015, Journal of Causal Inference 3: 1–24) and Cattaneo, Titiunik, and Vazquez-Bare (2016, Working Paper, University of Michigan, http://www-personal.umich.edu/∼titiunik/papers/ CattaneoTitiunikVazquezBare2015_wp.pdf). Assuming a known assignment mechanism for units close to the RD cutoff, these functions implement a variety of procedures based on randomization inference techniques. First, the rdrandinf command uses randomization methods to conduct point estimation, hypothesis testing, and confidence interval estimation under different assumptions. Second, the rdwinselect command uses finite-sample methods to select a window near the cutoff where the assumption of randomized treatment assignment is most plausible. Third, the rdsensitivity command uses randomization techniques to conduct a sequence of hypothesis tests for different windows around the RD cutoff, which can be used to assess the sensitivity of the methods and to construct confidence intervals by inversion. Finally, the rdrbounds command implements Rosenbaum (2002, Observational Studies [Springer]) sensitivity bounds for the context of RD designs under local randomization. Companion R functions with the same syntax and capabilities are also provided.

We investigate the effects of variations in the value of the charitable contribution deduction on nonprofit firm behavior, including exploring for the first time the effects of the tax price of giving on fund-raising. We find that a 1 percent increase in tax subsidies is correlated with a 2.0 percent increase in fund-raising, while the elasticity of real charitable output to changes in tax price is less than one in absolute value for most firms. We derive a new equation for treasury efficiency in the presence of fund-raising and find that while our point estimates still support treasury efficiency, our confidence intervals are wide enough to allow some possibility that the deduction is not cost effective. Further, the modest elasticity of charitable output to tax price implies that tax subsidies can crowd out other revenue sources, such that the efficacy of the subsidy depends on the relative efficiency of these alternative sources.

Comparison of treatment differences in incidence rates is an important objective of many clinical trials. However, often the proportion is affected by covariates, and the adjustment of the predicted proportion is made using logistic regression. It is desirable to estimate the treatment differences in proportions adjusting for the covariates, similarly to the comparison of adjusted means in analysis of variance. Because of the correlation between the point estimates in the different treatment groups, the standard methods for constructing confidence intervals are inadequate. The problem is more difficult in the binary case, as the comparison is not uniquely defined, and the sampling distribution more difficult to analyze. Four procedures for analyzing the data are presented, which expand upon existing methods and generalize the link function. It is shown that, among the four methods studied, the resampling method based on the exact distribution function yields a coverage rate closest to the nominal.

Plausible values can be used to either estimate population-level statistics or compute point estimates of latent variables. While it is well known that five plausible values are usually sufficient for accurate estimation of population-level statistics in large-scale surveys, the minimum number of plausible values needed to obtain accurate latent variable point estimates is unclear. This is especially relevant when an item response theory (IRT) model is estimated with MCMC (Markov chain Monte Carlo) methods in Mplus and point estimates of the IRT ability parameter are of interest, as Mplus only estimates the posterior distribution of each ability parameter. In order to obtain point estimates of the ability parameter, a number of plausible values can be drawn from the posterior distribution of each individual ability parameter and their mean (the posterior mean ability estimate) can be used as an individual ability point estimate. In this note, we conducted a simulation study to investigate how many plausible values were needed to obtain accurate posterior mean ability estimates. The results indicate that 20 is the minimum number of plausible values required to obtain point estimates of the IRT ability parameter that are comparable to marginal maximum likelihood estimation(MMLE)/expected a posteriori (EAP) estimates. A real dataset was used to demonstrate the comparison between MMLE/EAP point estimates and posterior mean ability estimates based on different number of plausible values.

There is growing interest in "customer-based corporate valuation," explicitly tying the value of a firm's customer base to its overall financial valuation using publicly disclosed data. While much progress has been made in building a well-validated customer-based valuation model for contractual (or subscription-based) firms, there has been little progress for non-contractual firms. Non-contractual businesses have more complex transactional patterns because customer churn is not observed, and customer purchase timing and spend amounts are more irregular. Furthermore, publicly disclosed data are aggregated over time and across customers, are often censored, and may vary from firm to firm, making it harder to estimate models for customer acquisition, ordering, and spend per order. The authors develop a general customer-based valuation methodology for non-contractual firms which accounts for these issues. They apply this methodology to publicly disclosed data from e-commerce retailers Overstock.com and Wayfair, providing valuation point estimates and valuation intervals for the firms, and comparing the unit economics of newly acquired customers.

Background: The primary purpose of prescription drug labeling is to give healthcare professionals the information needed to prescribe drugs appropriately. Therefore, labeling typically reports the effects that the treatment might be expected to have on several efficacy measures, including not only the primary endpoint used to establish effectiveness but also a number of key secondary endpoints that are important to practitioners and patients. Methods: One possible regulatory approach to drug labeling is to include results on important secondary efficacy endpoints in labeling only if there is statistical evidence of a treatment effect and a clinically meaningful estimated effect. We evaluate the statistical consequences of this approach by deriving and discussing the potential bias in point estimates and deviation from nominal coverage in confidence intervals that are reported in labeling. Results: Such an approach can lead to substantial conditional bias in point estimates (toward spuriously greater effects than the truth) and undercoverage in confidence intervals. Conclusion: These statistical properties may have important and undesirable regulatory and public health implications. We discuss an alternative approach to include results in labeling for a selected set of reliably ascertained, clinically important endpoints whether or not there is evidence of a treatment effect.

Background: Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Methods: Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith’s large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss–Cuzick estimator, and intervals constructed using a bootstrap- t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap- t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. Results: When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith’s method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (Conclusion: This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith’s method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (

Clustered data arise in many settings, particularly within the social and biomedical sciences. For example, multiple-source reports are commonly collected in child and adolescent psychiatric epidemiologic studies where researchers use various informants (for instance, parents and adolescents) to provide a holistic view of a subject's symptoms. Fitzmaurice et al. (1995, American Journal of Epidemiology 142: 1194–1203) have described estimation of multiple-source models using a standard generalized estimating equation (GEE) framework. However, these studies often have missing data because additional stages of consent and assent are required. The usual GEE is unbiased when data are missing completely at random in the context of Little and Rubin (2002, Statistical Analysis with Missing Data [Wiley]). This is a strong assumption that may not be tenable. Other options, such as the weighted GEE, are computationally challenging when missingness is nonmonotone. Multiple imputation is an attractive method to fit incomplete data models while requiring only the less restrictive missing-at-random assumption. Previously, estimation of partially observed clustered data was computationally challenging. However, recent developments in Stata have facilitated using them in practice. We demonstrate how to use multiple imputation in conjunction with a GEE to investigate the prevalence of eating disorder symptoms in adolescents as reported by parents and adolescents and to determine the factors associated with concordance and prevalence. The methods are motivated by the Avon Longitudinal Study of Parents and their Children, a cohort study that enrolled more than 14,000 pregnant mothers in 1991–92 and has followed the health and development of their children at regular intervals. While point estimates for the missing-at-random model were fairly similar to those for the GEE under missing completely at random, the missing-at-random model had smaller standard errors and required less stringent assumptions regarding missingness.

This article describes the user-written command iop to estimate ex-ante inequality of opportunity for different types of variables. Inequality of opportunity is the part of inequality that is due to circumstances beyond the control of the individual. Therefore, it is the ethically offensive part of inequality. Several estimation procedures have been proposed over the past years, and iop is a comprehensive and easy-to-use command that implements many of them. It handles continuous, dichotomous, and ordered variables. In addition to the point estimates, iop also provides bootstrap standard errors and two decomposition methods.

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box–Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples ( n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.

In cost-effectiveness (CE) Markov models, heterogeneity in the patient population is not automatically taken into account. We aimed to compare methods of dealing with heterogeneity on estimates of CE, using a case study in chronic obstructive pulmonary disease (COPD). We first present a probabilistic sensitivity analysis (PSA) in which we sampled only from distributions representing parameter uncertainty. This ignores any heterogeneity. Next, we explored heterogeneity by presenting results for subgroups, using a method that samples parameter uncertainty simultaneously with heterogeneity in a single-loop PSA. Finally, we distinguished parameter uncertainty from heterogeneity in a double-loop PSA by performing a nested simulation within each PSA iteration. Point estimates and uncertainty differed substantially between methods. The incremental CE ratio (ICER) ranged from €4900 to €13,800. The single-loop PSA led to a substantially different shape of the CE plane and an overestimation of the uncertainty compared with the other 3 methods. The CE plane for the double-loop PSA showed substantially less uncertainty and a stronger negative correlation between the difference in costs and the difference in effects compared with the other methods. This came at the cost of higher calculation times. Not accounting for heterogeneity, subgroup analysis and the double-loop PSA can be viable options, depending on the decision makers’ information needs. The single-loop PSA should not be used in CE research. It disregards the fundamental differences between heterogeneity and sampling uncertainty and overestimates uncertainty as a result.

Spatio-temporal disease mapping comprises a wide range of models used to describe the distribution of a disease in space and its evolution in time. These models have been commonly formulated within a hierarchical Bayesian framework with two main approaches: an empirical Bayes (EB) and a fully Bayes (FB) approach. The EB approach provides point estimates of the parameters relying on the well-known penalized quasi-likelihood (PQL) technique. The FB approach provides the posterior distribution of the target parameters. These marginal distributions are not usually available in closed form and common estimation procedures are based on Markov chain Monte Carlo (MCMC) methods. However, the spatio-temporal models used in disease mapping are often very complex and MCMC methods may lead to large Monte Carlo errors and a huge computation time if the dimension of the data at hand is large. To circumvent these potential inconveniences, a new technique called integrated nested Laplace approximations (INLA), based on nested Laplace approximations, has been proposed for Bayesian inference in latent Gaussian models. In this paper, we show how to fit different spatio-temporal models for disease mapping with INLA using the Leroux CAR prior for the spatial component, and we compare it with PQL via a simulation study. The spatio-temporal distribution of male brain cancer mortality in Spain during the period 1986–2010 is also analysed.

Background: To increase power to detect a treatment effect, trials may combine multiple endpoints such as survival, myocardial infarctions, and strokes. When such trials are used to define the uncertainty associated with input parameters in an economic model, the output uncertainty will depend on the way in which the dependency between individual endpoints is modeled. Objective: To develop a flexible approach to model the interrelationship between individual components of a combined endpoint. Methods: A standard independent Dirichlet approach is compared with a dependent Dirichlet approach and logistic approaches. The logistic approaches use a link between the various endpoints by either an observed clinical variable (cholesterol) or a latent one. The logistic and Dirichlet methods are compared using 6 statin trials including 5 endpoints: myocardial infarction (MI), stroke, fatal MI, fatal stroke, and other cardiovascular death. The results are compared using the point estimates and uncertainty in a simplified cardiovascular model to calculate point estimates and uncertainty of estimated incremental life-years when applying probabilistic sensitivity analysis. The influence of the link between endpoints is tested by changing the prior in the logistic approaches. Results: The dependent Dirichlet approach reduces uncertainty up to 29% and changes the point estimate by up to 28%. The logistic approach with uninformative priors does not affect the uncertainty and point estimates. When strong priors are used, the uncertainty margins get smaller (up to 49%) and point estimates vary more. Including information about cholesterol has limited impact. Conclusions: The logistic approaches offer a flexible way to reflect one’s beliefs about the interrelationships between individual endpoints, potentially decreasing uncertainty margins. The approach works equally well with and without data concerning the underlying disease process.

In this article, we explain how to calculate adjusted risk ratios and risk differences when reporting results from logit, probit, and related nonlinear models. Building on Stata's margins command, we create a new postestimation command, adjrr, that calculates adjusted risk ratios and adjusted risk differences after running a logit or probit model with a binary, a multinomial, or an ordered outcome. adjrr reports the point estimates, delta-method standard errors, and 95% confidence intervals and can compute these for specific values of the variable of interest. It automatically adjusts for complex survey design as in the fit model. Data from the Medical Expenditure Panel Survey and the National Health and Nutrition Examination Survey are used to illustrate multiple applications of the command.

Purpose. Multiple imputation (MI) has been proposed for handling missing data in cost-effectiveness analyses (CEAs). In CEAs that use cluster randomized trials (CRTs), the imputation model, like the analysis model, should recognize the hierarchical structure of the data. This paper contrasts a multilevel MI approach that recognizes clustering, with single-level MI and complete case analysis (CCA) in CEAs that use CRTs. Methods. We consider a multilevel MI approach compatible with multilevel analytical models for CEAs that use CRTs. We took fully observed data from a CEA that evaluated an intervention to improve diagnosis of active labor in primiparous women using a CRT (2078 patients, 14 clusters). We generated scenarios with missing costs and outcomes that differed, for example, according to the proportion with missing data (10%–50%), the covariates that predicted missing data (individual, cluster-level), and the missingness mechanism: missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). We estimated incremental net benefits (INBs) for each approach and compared them with the estimates from the fully observed data, the “true” INBs. Results. When costs and outcomes were assumed to be MCAR, the INBs for each approach were similar to the true estimates. When data were MAR, the point estimates from the CCA differed from the true estimates. Multilevel MI provided point estimates and standard errors closer to the true values than did single-level MI across all settings, including those in which a high proportion of observations had cost and outcome data MAR and when data were MNAR. Conclusions. Multilevel MI accommodates the multilevel structure of the data in CEAs that use cluster trials and provides accurate cost-effectiveness estimates across the range of circumstances considered.

In this article, we employ a Bayesian framework to estimate parameter and model uncertainty for shape memory alloy bending actuators. The Bayesian framework provides parameter densities, instead of ordinary least-squares optimal point estimates. Bayes’ rule relates a posterior parameter density to a prior density and likelihood. However, the posterior density is difficult to calculate directly for high-dimensional parameter spaces. Markov chain Monte Carlo methods overcome this difficulty indirectly by creating a Markov chain whose stationary density is the posterior. In this article, we utilize the Delayed Rejection Adaptive Metropolis algorithm for estimating parameter uncertainty. The shape memory alloy bending actuator is modeled using the homogenized energy framework, a computationally efficient and accurate model for various transductive materials. The model is summarized, and techniques for estimating the heat transfer parameters are presented. An algorithmic approach to quantifying uncertainty is useful for numerous reasons. The anticipated use is to quantify uncertainty for robust control algorithms. Robust control is an area of considerable research for smart materials such as shape memory alloy; however, the source of uncertainty is rarely quantified. The methods employed here would greatly aid in the design of robust controllers.

Abtsrcat The purely sequential minimum risk point estimation procedure for the unknown parameter θ (> 0) in a Uniform (0; θ) population has been discussed in this article. This is developed under a squared error loss plus a linear cost function of sampling. The unknown parameter θ is estimated by means of four different estimators in the stopping rule, where as in the loss function two different unbiased estimators of θ are proposed. The unbiased estimators are randomly stopped versions of Χ n: n and X¯ n in either loss function. Performances of such estimators are compared. Clearly, using a randomly stopped version of X¯ n would amount to some loss of in- formation when compared with a corresponding randomly stopped largest sample order statistic under both loss functions and the stopping rules. In this paper, we explore a novel approach to recover any loss of information by fine-tuning the loss function and then properly tailoring the associated sequential methodologies. We examine how the sequential risks of our newly proposed methodologies would compare with those associated with the ex- isting sequential estimators and illustrate our methodologies with the help of a real dataset.

This article describes a new Stata command, tsb, for performing a stratified two-stage nonparametric bootstrap resampling procedure for clustered data. Estimates for uncertainty around the point estimate, such as standard error and confidence intervals, are derived from the resultant bootstrap samples. A shrinkage estimator proposed for correcting possible overestimation due to second-stage sampling is implemented as default. Although this command is written with cost effectiveness analyses alongside cluster trials in mind, it is applicable to the analysis of continuous endpoints in cluster trials more generally. The use of this command is exemplified with a case study of a cost effectiveness analysis undertaken alongside a cluster randomized trial. We also report bootstrap confidence interval coverage by using data from a published simulation study.

The major problem in the vibration-based damage detection field is still a limited number of sensors and the existence of uncertainties. In this paper, a new approach combines a multi-stage ANN model and statistical method to detect damage based on the limited number of sensors with consideration of uncertainties. The first stage of the ANN is used to predict the unmeasured mode shapes data based on limited measured modal data. The second stage ANN is devoted to predicting the damage location and severity using the complete modal data from the first-stage ANN. To incorporate the uncertainties in modal data, Gaussian noise is applied to the input variables and the probability of damage existence is calculated using Rosenblueth's point estimate method. The feasibility of the proposed method is demonstrated using an analytical model of a continuous two-span reinforced concrete slab. The application of a multi-stage ANN showed results having a high potential of overcoming the issue of using a limited number of sensors in structural health monitoring.

Variance-based importance measure has proven itself as an effective tool to reflect the effects of input variables on the output. Owing to the desirable properties, researchers have paid lots of attention to improving efficiency in computing a variance-based importance measure. Based on the theory of point estimate, this article proposes a new algorithm, decomposing the importance measure into inner and outer parts, and computing each part with a point estimate method. In order to discuss the impacts on the variance-based importance measure from distribution parameters of input variables, a new concept of kernel sensitivity of the variance-based importance measure is put forward, with solving algorithms respectively, based on numerical simulation and point estimate established as well. For cases where the performance function with independent and normally distributed input variables is expressed by a linear or quadratic polynomial without cross-terms, analytical results of the variance-based importance measure and the kernel sensitivity are derived. Numerical and engineering examples have been employed to illustrate the applicability of the proposed concept and algorithm.

It is shown that deviations of estimated from true values of item difficulty parameters, caused for example by item calibration errors, the neglect of randomness of item difficulty parameters, testlet effects, or rule-based item generation, can lead to systematic bias in point estimation of person parameters in the context of adaptive testing. This effect occurs even when the errors of the item difficulty parameters are themselves unbiased. Analytical calculations as well as simulation studies are discussed.

Precise identification of the time when a change in a clinical process has occurred enables experts to identify a potential special cause more effectively. In this article, we develop change point estimation methods for a clinical dichotomous process in the presence of case mix. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the odds ratio and logit of risk of a Bernoulli process. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted CUSUM and EWMA control charts. In comparison with alternative EWMA and CUSUM estimators, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities enhance when probability quantification, flexibility and generaliability of the Bayesian change point detection model are also considered. The Deviance Information Criterion, as a model selection criterion in the Bayesian context, is applied to find the best change point model for a given dataset where there is no prior knowledge about the change type in the process.

The limits of agreement (LoA) method proposed by Bland and Altman has become a standard for assessing agreement between different methods measuring the same quantity. Virtually, all method comparison studies have reported only point estimates of LoA due largely to the lack of simple confidence interval procedures. In this article, we address confidence interval estimation for LoA when multiple measurements per individual are available. Separate procedures are proposed for situations when the underlying true value of the measured quantity is assumed changing and when it is perceived as stable. A fixed number of replicates per individual is not needed for the procedures to work. As shown by the worked examples, the construction of these confidence intervals requires only quantiles from the standard normal and chi-square distributions. Simulation results show the proposed procedures perform well. A SAS macro implementing the methods is available on the publisher's website.

We quantified determinants of international migratory inflows to 17 Western countries and outflows from 13 of these countries between 1950 and 2007 in 77,658 observations from multiple sources using panel-data analysis techniques. To construct a quantitative model that could be useful for demographic projection, we analyzed the logarithm of the number of migrants (inflows and outflows separately) as dependent variables in relation to demographic, geographic, and social independent variables. The independent variables most influential on log inflows were demographic [log population of origin and destination and log infant mortality rate (IMR) of origin and destination] and geographic (log distance between capitals and log land area of the destination). Social and historical determinants were less influential. For log outflows from the 13 countries, the most influential independent variables were log population of origin and destination, log IMR of destination, and log distance between capitals. A young age structure in the destination was associated with lower inflows while a young age structure in the origin was associated with higher inflows. Urbanization in destination and origin increased international migration. IMR affected inflows and outflows significantly but oppositely. Being landlocked, having a common border, having the same official language, sharing a minority language, and colonial links also had statistically significant but quantitatively smaller effects on international migration. Comparisons of models with different assumed correlation structures of residuals indicated that independence was the best assumption, supporting the use of ordinary-least-squares estimation techniques to obtain point estimates of coefficients. [ABSTRACT FROM AUTHOR]

A new Stata command, simsum, analyzes data from simulation studies. The data may comprise point estimates and standard errors from several analysis methods, possibly resulting from several different simulation settings. simsum can report bias, coverage, power, empirical standard error, relative precision, average model-based standard error, and the relative error of the standard error. Monte Carlo errors are available for all of these estimated quantities.

This paper uses a detailed employer–employee dataset to test the effects of low-income-country import competition on the earnings of workers in manufacturing industries in Canada. From 1996 to 2001 earnings inequality increased: the wage gap between manufacturing workers with a university degree and those without a high school diploma rose from 35% to 43%. Greater import competition is found to explain part of the growing wage gap, consistent with Stolper–Samuelson effects. Point estimates suggest that these effects increased slightly over the period of study and are strongest in Quebec, Ontario, and British Columbia, provinces with the highest low-income-country import-penetration rates. However, the results also suggest (i) the relative wage effects of increased import competition are not as ‘clear cut’ as predicted by the Stolper–Samuelson theorem and (ii) that other individual and plant-level factors are important determinants of the structure of earnings inequality in Canada.

Bivariate random effect models are currently one of the main methods recommended to synthesize diagnostic test accuracy studies. However, only the logit transformation on sensitivity and specificity has been previously considered in the literature. In this article, the authors consider a bivariate generalized linear mixed model to jointly model the sensitivities and specificities, and they discuss the estimation of the summary receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC). As the special cases of this model, the authors discuss the commonly used logit, probit, and complementary log-log transformations. To evaluate the impact of misspecification of the link functions on the estimation, they present 2 case studies and a set of simulation studies. Their study suggests that point estimation of the median sensitivity and specificity and AUC is relatively robust to the misspecification of the link functions. However, the misspecification of link functions has a noticeable impact on the standard error estimation and the 95% confidence interval coverage, which emphasizes the importance of choosing an appropriate link function to make statistical inference.

Although landline telephone household surveys often draw inference about the general population, a proportion with only cell phones is excluded. In the United States, like in much of the world, this proportion is substantial and increasing, providing potential for coverage bias. Studies have looked at bias in means, but undercoverage can affect other essential statistics. The precision of point estimates can be biased, leading to erroneous conclusions. Research examining multivariate relationships will be further affected by bias in associations. A national landline telephone survey was conducted, followed by a survey of adults with only cell phones. In addition to estimates of means and proportions, differences were found for variances and associations. Bias in some point estimates was reduced through poststratification but became larger and in opposite direction for others. Different uses of survey data can be affected by omitting the cell-only population, and reliance on postsurvey adjustments can be misleading.

This paper, which is the first large-scale application of respondent-driven sampling (RDS) to nonhidden populations, tests three factors related to RDS estimation against institutional data using two Web RDS samples of university undergraduates. First, two methods of calculating RDS point estimates are compared. RDS estimates calculated using both methods coincide closely, but variance estimation, especially for small groups, is problematic for both methods. In one method, the bootstrap algorithm used to generate confidence intervals is found to underestimate variance. In the other method, where analytical variance estimation is possible, confidence intervals tend to overestimate variance. Second, RDS estimates are found to be robust against varying measures of individual degree. Results suggest the standard degree measure currently employed in most RDS studies is among the best-performing degree measures. Finally, RDS is found to be robust against the inclusion of out-of-equilibrium data. The results show that valid point estimates can be generated with RDS analysis using real data, but that further research is needed to improve variance estimation techniques.

Visual line graphs are a prevalent form of communication as they provide a pictorial means to display relationships between entities. As such, understanding the cognitive resources required in processing line graphs would inform designers how to optimize the use of graphical displays. This study systematically investigated how graph task performance changes as a function of attention allocation (full or divided) and concurrent memory task (spatial or verbal). Twenty-four younger adults (mean age 19.2 years) completed either a trend comparison task or a point estimation task and a concurrent spatial or verbal task. Trend comparison performance did not significantly differ between the full and divided attention conditions; mean performance for all conditions was over 90% accurate. Interestingly, participants' point estimation performance was significantly better for the two divided attention conditions compared to the full attention condition which may be attributed to a motivational or stimulus effect. This study provides a base from which more research can be conducted to understand the verbal and spatial resources required in graph comprehension.

This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it jointly with its related confidence interval. The results of the study demonstrate that there is little empirical support for the use of .05 or any other value as universal cutoff values to determine adequate model fit, regardless of whether the point estimate is used alone or jointly with the confidence interval. The authors' analyses suggest that to achieve a certain level of power or Type I error rate, the choice of cutoff values depends on model specifications, degrees of freedom, and sample size.

The authors examine the magnitude of health benefits from urban design characteristics that are associated with increased walking. Using geocoded travel diary data from Portland, Oregon, regression analyses give information on the magnitude and statistical significance of the link between urban design variables and two-day walking distances. From the coefficient point estimates, the authors link to the health literature to give information on how many persons would realize health benefits, in the form of reductions in mortality risk, from walking increases associated with urban design changes. Using a cost-benefit analysis framework, they give monetized estimates of the health benefits of various urban design changes. The article closes with suggestions about how the techniques developed can be applied to other cost-benefit analyses of the health benefits of planning projects that are intended to increase walking.

The authors conducted Monte Carlo simulations to compare the Hedges and Olkin, the Hunter and Schmidt, and a refinement of the Aguinis and Pierce meta-analytic approaches for estimating moderating effects of categorical variables. The simulation examined binary moderator variables (e.g., gender—male, female; ethnicity—majority, minority). The authors compared the three meta-analytic methods in terms of their point estimation accuracy and Type I and Type II error rates. Results provide guidelines to help researchers choose among the three meta-analytic techniques based on theory (i.e., exploratory vs. confirmatory research) and research design considerations (i.e., degree of range restriction and measurement error).

The standard error of the maximum likelihood ability estimator is commonly estimated by evaluating the test information function at an examinee's current maximum likelihood estimate (a point estimate) of ability. Because the test information function evaluated at the point estimate may differ from the test information function evaluated at an examinee's true ability value, the estimated standard error may be biased under certain conditions. This is of particular concern in adaptive testing because the height of the test information function is expected to be higher at the current estimate of ability than at the actual value of ability. This article proposes using the posterior-weighted test information function in computing the standard error of the maximum likelihood ability estimator for adaptive test sessions. A simulation study showed that the proposed approach provides standard error estimates that are less biased and more efficient than those provided by the traditional point estimate approach.

Rudner (2001, 2005) described an expected classification accuracy index for determining the asymptotic expectation of accuracy of classifications of examinees into score categories. This article expands on that exposition by evaluating the index as it is likely to be used in practice (as a point estimate of classification accuracy), provides a related index of expected proportion in each score category, derives a measurement error—based standard error of expected proportion in each category, applies the methods to a large-scale state assessment database, and analyzes the benefits and limitations of the methodology. This study demonstrates that Rudner's index as a point estimate is slightly positively biased but nevertheless useful for groups with reasonably large numbers of examinees. It also reinforces the inappropriateness of estimating proportions in score categories for many small groups.

“Uncertainty analysis” is a relatively new term in traffic capacity analysis, but it is routine in other areas of civil engineering. Three methods are proposed to examine the uncertainty of Highway Capacity Manual models caused by variance in the inputs: one-at-a-time, point estimate, and Monte Carlo simulation. These methods have different levels of computational intensity and input requirements. They are recommended for three levels of detail in probabilistic analysis, depending on the application. Correspondingly, the results of probabilistic analysis vary from simple upper and lower values to a comprehensive output with mean values, standard deviations, confidence intervals, and delay distributions. The methods were applied to a signalized intersection to demonstrate the application of the three levels of analysis. Field data were from an actuated signalized intersection in Honolulu, Hawaii. Application of the three methods is illustrated in detail. Although the probabilistic method is considered to be more robust than the existing deterministic method, the former is not recommended for application to every project. Options are given for conditions under which the probabilistic method should be applied and the appropriate level of probabilistic analysis.

Benefit–cost analyses of intelligent transportation systems (ITS) have produced point estimates of the benefit–cost ratios of ITS deployments. The need to perform risk analyses to account for the uncertainty in ITS benefit and cost parameters is discussed. It is recommended that the analyses be based on overall assessments of the combination of point estimates, confidence limits, and probability distributions of the benefits, costs, and benefit–cost ratios. Two methods are presented for performing risk analyses with Monte Carlo simulation. The first allows the calculation of the parameters required for the risk analysis procedure in the ITS Deployment Analysis System (IDAS) sketch planning tool. No methodology has been proposed for this calculation in the literature. The second method is a generalized risk analysis procedure that can be implemented in sketch planning tools. The procedure was implemented in this study as part of the screening analysis for ITS. The results indicate that for the investigated case studies, procedures that base the risk analyses on varying the output variables (as is the case in IDAS) will result in benefit–cost ratios with medians (50th percentiles) that are close to those obtained with procedures that are based on varying the input variables. However, the confidence limits produced may be different. The results also indicate that risk analyses produce wide confidence intervals of the estimated benefit–cost ratios, reflecting the high levels of uncertainty in the values of ITS benefit and cost parameters reported in national benefit–cost studies and databases.