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In this article we introduce randomized repeated significance tests (RSTs), based on scan statistics, for detecting a local change in a parameter of a distribution for one and two dimensional discrete data. When the size of the window where a local change in the parameter has occurred is known, a randomized RST based on a fixed window scan statistic is proposed. When the size of the window where a local change in the parameter has occurred is unknown, a randomized RST based on the minimum P-value scan statistic is developed. Simulation based methods are used to implement these randomized RSTs. Numerical results for one and two dimensional data, generated from Bernoulli and Poisson distributions, for selected values of model parameters, demonstrate the effectiveness of the randomized RSTs in detecting a local change in the parameter of the respective model. [ABSTRACT FROM AUTHOR]

We present two novel approaches to variance estimation of semi-parametric efficient point estimators of the treatment-specific mean: (i) a robust approach that directly targets the variance of the influence function (IF) as a counterfactual mean outcome and (ii) a modified non-parametric bootstrap-based approach. The performance of these approaches to variance estimation is compared to variance estimation based on the sample variance of the empirical IF in simulations across different levels of positivity violations and treatment effect sizes. In this article, we focus on estimation of the nuisance parameters using correctly specified parametric models for the treatment mechanism in order to highlight the challenges posed by violation of positivity assumptions (distinct from the challenges posed by non-parametric estimation of the nuisance parameters). Results demonstrate that (1) variance estimation based on the empirical IF may provide highly anti-conservative confidence interval coverage (as reported previously), (2) the proposed robust approach to variance estimation in this setting provides conservative coverage, and (3) the proposed modified bootstrap maintains close to nominal coverage and improves power. In the appendix, we (a) generalize the robust approach of estimating variance to marginal structural working models and (b) provide a proof of the consistency of the targeted minimum loss-based estimation bootstrap.

Scientists need to compare the support for models based on observed phenomena. The main goal of the evidential paradigm is to quantify the strength of evidence in the data for a reference model relative to an alternative model. This is done via an evidence function, such as ΔSIC, an estimator of the sample size scaled difference of divergences between the generating mechanism and the competing models. To use evidence, either for decision making or as a guide to the accumulation of knowledge, an understanding of the uncertainty in the evidence is needed. This uncertainty is well characterized by the standard statistical theory of estimation. Unfortunately, the standard theory breaks down if the models are misspecified, as is commonly the case in scientific studies. We develop non-parametric bootstrap methodologies for estimating the sampling distribution of the evidence estimator under model misspecification. This sampling distribution allows us to determine how secure we are in our evidential statement. We characterize this uncertainty in the strength of evidence with two different types of confidence intervals, which we term “global” and “local.” We discuss how evidence uncertainty can be used to improve scientific inference and illustrate this with a reanalysis of the model identification problem in a prominent landscape ecology study using structural equations.

Interests about the fine underpinnings of cardiovascular beat-by-beat variability have historical roots. Over the last decades, various aspects of the relationships between arterial pressure and heart period were taken as a proxy of the baroreflex in physiology and medicine, stimulating the interest of investigators in several interconnected scientific fields, in particular, bioengineering, neurophysiology, and clinical medicine. Studies of the overall system facilitated the emergence of a simplified negative (vagal) feedback model of the baroreflex and overshadowed the simultaneous interaction with excitatory, sympathetic positive-feedback mechanisms that would, however, better suit the model of a “paired antagonistic (parasympathetic/sympathetic) innervation of the internal organs.” From the bioengineering side, the simplicity of obtaining the series of subsequent RR intervals stimulated the analysis of beat-by-beat variations, providing a multitude of heart rate variability (HRV) indices considered as proxies of the underlying sympatho-vagal balance, and participating to the management of several important clinical conditions, such as hypertension. In this context, advanced statistical methods, used in an integrated manner and controlling for age and gender biases, might help shed new light on the relationship between cardiac baroreflex, assessed by the frequency domain index α, and the HRV indices with the varying of systolic arterial pressure (SAP) levels. The focus is also on a novel unitary Autonomic Nervous System Index (ANSI) built as a synthesis of HRV considering its three most informative proxies [RR, RR variance, and the rest-stand difference in the normalized power of low-frequency (LF) variability component]. Data from a relatively large set of healthy subjects (n = 1154) with a broad range of SAP [from normal (nNt = 778) to elevated (nHt = 232)] show that, e.g., α and ANSI significantly correlate overall (r = 0.523, p < 0.001), and that this correlation is lower in hypertensives (r = 0.444, p < 0.001) and higher in pre-hypertensives (r = 0.618, p < 0.001) than in normotensives (r = 0.5, p < 0.001). That suggests the existence of curvilinear “umbrella” patterns that might better describe the effects of the SAP states on the relationships between baroreflex and HRV. By a mix of robust, non-parametric and resampling statistical techniques, we give empirical support to this study hypothesis and show that the pre-hypertensive group results at the apex/bottom in most of the studied trends.

Interests about the fine underpinnings of cardiovascular beat-by-beat variability have historical roots. Over the last decades, various aspects of the relationships between arterial pressure and heart period were taken as a proxy of the baroreflex in physiology and medicine, stimulating the interest of investigators in several interconnected scientific fields, in particular, bioengineering, neurophysiology, and clinical medicine. Studies of the overall system facilitated the emergence of a simplified negative (vagal) feedback model of the baroreflex and overshadowed the simultaneous interaction with excitatory, sympathetic positive-feedback mechanisms that would, however, better suit the model of a "paired antagonistic (parasympathetic/sympathetic) innervation of the internal organs." From the bioengineering side, the simplicity of obtaining the series of subsequent RR intervals stimulated the analysis of beat-by-beat variations, providing a multitude of heart rate variability (HRV) indices considered as proxies of the underlying sympatho-vagal balance, and participating to the management of several important clinical conditions, such as hypertension. In this context, advanced statistical methods, used in an integrated manner and controlling for age and gender biases, might help shed new light on the relationship between cardiac baroreflex, assessed by the frequency domain index α, and the HRV indices with the varying of systolic arterial pressure (SAP) levels. The focus is also on a novel unitary Autonomic Nervous System Index (ANSI) built as a synthesis of HRV considering its three most informative proxies [RR, RR variance, and the rest-stand difference in the normalized power of low-frequency (LF) variability component]. Data from a relatively large set of healthy subjects (n = 1154) with a broad range of SAP [from normal (n Nt = 778) to elevated (n Ht = 232)] show that, e.g., α and ANSI significantly correlate overall (r = 0.523, p < 0.001), and that this correlation is lower in hypertensives (r = 0.444, p < 0.001) and higher in pre-hypertensives (r = 0.618, p < 0.001) than in normotensives (r = 0.5, p < 0.001). That suggests the existence of curvilinear "umbrella" patterns that might better describe the effects of the SAP states on the relationships between baroreflex and HRV. By a mix of robust, non-parametric and resampling statistical techniques, we give empirical support to this study hypothesis and show that the pre-hypertensive group results at the apex/bottom in most of the studied trends. [ABSTRACT FROM AUTHOR]

The likelihood of a generalized linear mixed model (GLMM) often involves high-dimensional integrals, which in general cannot be computed explicitly. When direct computation is not available, method of simulated moments (MSM) is a fairly simpleway to estimate the parameters of interest. In this research, we compared parametric bootstrap (PB) and nonparametric bootstrapmethods (NPB) in estimating the standard errors of MSM estimators for GLMM. Simulation results show that when the group size is large, the PB and NPB perform similarly; when group size is medium, NPB performs better than PB in estimating standard errors of the mean. [ABSTRACT FROM AUTHOR]

Background: The prevalence of type 2 diabetes mellitus (T2DM) is increasing in Iran. This study determined the prevalence of undiagnosed type 2 diabetes mellitus in apparently normal individuals in Hamadan, west of Iran. Study design: A cross-sectional study. Methods: A sample of 106 apparently normal volunteers aged 18 yr and more were enrolled, and T2DM was diagnosed using hemoglobin A1c (HbA1c) levels from 2015-2016. The nonparametric bootstrap method was used to eliminate the undesirable effect of small sample size on the estimation of standard error of multiple logistic regression coefficients and confidence interval for the prevalence of undiagnosed T2DM. Results: Overall, 23 (21.69%) were male. The mean (±sd) age of the participants was 43.76 ± 14.01 year. In 78.3% of individuals, HbA1c level was within normal range (<5.7), 13.21% was in the range of 5.7-6.4 (undiagnosed pre-T2DM), and 8.49% was ≥6.5 (undiagnosed T2DM). Multiple logistic regression gave the characteristic distribution of volunteers such as sedentary hour (P=0.001), family history of diabetes (P=0.001), smoking (P=0.002), and age (P=0.012) had the odds on the significant effect on undiagnosed T2DM. Conclusions: The prevalence of undiagnosed T2DM among apparently normal individuals in Hamadan was relatively high. Addition to age, factors such as sedentary, exposure to smoking and having a history of diabetes in family can be a prognosis for undiagnosed T2DM in apparently normal individuals. [ABSTRACT FROM AUTHOR]

For enhancing the quality and productivity, the use of process capability index (PCI) has become significant in statistical process control, designed to quantify the relation between the actual performance of the process and its specified requirements. Confidence interval is an important part of PCI because it is an estimated value and provides much more information about the population characteristic of interest than does a point estimate. In this article, bootstrap confidence intervals of non-normal PCI , is studied through simulation when the underlying distribution is exponential power distribution. Maximum likelihood method is used to estimate the parameters of the model. Four (parametric as well as non-parametric) bootstrap confidence intervals, namely standard bootstrap (s-boot), percentile bootstrap (p-boot), Student’s t bootstrap (t-boot), and bias-corrected percentile bootstrap (-boot), are considered for obtaining confidence intervals of . A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the bootstrap confidence intervals. Simulation results showed that among these (s-boot, p-boot, t-boot, and -boot) confidence intervals, the performances of the s-boot confidence intervals is the best in terms of overage probabilities. Finally, three real data-sets are analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]

Various charts such as | S|, W, and G are used for monitoring process dispersion. Most of these charts are based on the normality assumption, while exact distribution of the control statistic is unknown, and thus limiting distribution of control statistic is employed which is applicable for large sample sizes. In practice, the normality assumption of distribution might be violated, while it is not always possible to collect large sample size. Furthermore, to use control charts in practice, the in-control state usually has to be estimated. Such estimation has a negative effect on the performance of control chart. Non-parametric bootstrap control charts can be considered as an alternative when the distribution is unknown or a collection of large sample size is not possible or the process parameters are estimated from a Phase I data set. In this paper, non-parametric bootstrap multivariate control charts | S|, W, and G are introduced, and their performances are compared against Shewhart-type control charts. The proposed method is based on bootstrapping the data used for estimating the in-control state. Simulation results show satisfactory performance for the bootstrap control charts. Ultimately, the proposed control charts are applied to a real case study. [ABSTRACT FROM AUTHOR]

Purpose: Taking into account the large area of citrus in Spain and the impacts generated by agriculture, reducing the environmental impact of this crop represents an important goal. This study attempts to compare the environmental impact of two citrus cropping systems, organic and conventional, in the region of Valencia (Spain), and to assess the variability within both farming systems in order to highlight the influence of management practices on the environmental performance. Methods: A survey was carried out on citrus farmers, 145 corresponding to organic production and 122 to conventional. Life cycle assessment (LCA) was used to estimate the environmental impacts of farms and the contribution of each production stage to impacts. Two functional units (FUs), mass- and area-based, were chosen. The variability and confidence intervals of the average impact results were assessed by means of a bootstrap technique. Finally, a k-means cluster analysis was performed to identify groups of farms with comparable impact levels. Results and discussion: The mean impact values of the conventional farm sample were higher than those of the organic farms, when using 1 ha year as FU, whereas for the FU of 1 kg no differences were found for some impact categories. Most of the impact results were also observed to be highly variable. The distribution of the mean after the bootstrap confirmed both the variability of the impacts and the differences between the farming systems. The cluster analysis determined two groups via their impact categories. Cluster-1, which showed higher impacts, was made up of part of the conventional farms while cluster-2 included the remaining conventional farms and all the organic ones. No difference in yield was found between the conventional farms of both clusters. Conclusions: Bootstrapped LCA offers the ability to assess the variability of the impacts, regardless of the sample size and the non-normal impact distributions. The cluster analysis shows that conventional farms can attain similar impacts than the organic ones, while maintaining the yield. FU selection is crucial, since a mass-based FU reduces the difference in the environmental performance between conventional and organic farms. To attain a more sustainable citrus farming, a careful selection of the management practices is needed. [ABSTRACT FROM AUTHOR]

In line with recent developments in the statistical analysis of functional data, we develop the semiparametric functional auto-regressive modelling approach to the density forecasting analysis of national rates of inflation by using sectoral inflation rates in the UK over the period January 1997-September 2013. The pseudo-out-of-sample forecasting evaluation and test results provide an overall support to superior performance of our proposed models over the aggregate auto-regressive models and their statistical validity. The fan chart analysis and the probability event forecasting exercise provide further support for our approach in a qualitative sense, revealing that the modified functional auto-regressive models can provide a complementary tool for generating the density forecast of inflation, and for analysing the performance of a central bank in achieving announced inflation targets. As inflation targeting monetary policies are usually set with recourse to the medium-term forecasts, our proposed work may provide policy makers with an invaluably enriched information set. [ABSTRACT FROM AUTHOR]

We apply techniques from the event probability forecasting literature to the analysis of spillover scenarios in economic and financial networks. A simple spillover scenario is expressed as an inequality constraint with respect to a single spillover measure. More complex spillover scenarios can be defined as combinations of simple scenarios. The scenario probabilities are evaluated using a non-parametric bootstrap. We use our technique to study credit risk transmission among a group of 18 countries over the 2006–2010 period. We show that abrupt changes in the probabilities of "crisis scenarios" accurately map on to key events during the Global Financial Crisis. • We develop a method for the analysis of spillover scenarios in network models. • Scenarios are defined by inequalities that apply to one or more spillover measures. • Scenario probabilities are evaluated using a non-parametric bootstrap. • We study sovereign–financial credit risk transmission from 2006–2010. • Event probabilities map onto historical events during the global financial crisis. [ABSTRACT FROM AUTHOR]

This paper presents nonparametric two-sample bootstrap tests formeans of randomsymmetric positivedefinite (SPD) matrices according to two differentmetrics: the Frobenius (or Euclidean)metric, inherited from the embedding of the set of SPD metrics in the Euclidean set of symmetric matrices, and the canonical metric, which is defined without an embedding and suggests an intrinsic analysis. A fast algorithm is used to compute the bootstrap intrinsic means in the case of the latter. The methods are illustrated in a simulation study and applied to a two-group comparison of means of diffusion tensors (DTs) obtained from a single voxel of registered DT images of children in a dyslexia study.