Bootstrap Estimates Research Articles

Impact on healthcare costs of adding telemedicine to a multidisciplinary disease management program for heart failure: a sub-analysis of the iCOR trial

Abstract Background Impact of telemedicine on heart failure outcomes has shown conflicting results. Little is known about the effect of these programs on mid-term healthcare costs. Purpose To evaluate the cost-efficacy of a comprehensive telemonitoring program within the vulnerable post-discharge period after an acute heart failure admission. Methods The insuficiència cardiaca Optimizació Remota (iCOR) trial was a single-centre, randomized, open-label study designed to evaluate the efficacy of the addition of telemonitoring and teleintervention to a specialized multidisciplinary nurse-led heart failure (HF) program. Adult patients with a recent hospital admission for acute HF and an acceptable social support and cognitive ability were recruited. This sub-analysis evaluates the effect of telemedicine on mid-term healthcare costs, including clinical events, drugs and devices, technology purchase and maintenance, outpatient care, days of hospitalization, procedures and complementary tests. t-test via bootstrap estimation is used to compare differences between the telemedicine (TM) and the usual care (UC) groups. Results 81 patients were assigned to the TM group and 97 patients to the UC group. Mean age was 74 years, more than 50% had HF with preserved ejection fraction and 25% were frail. Baseline characteristics were well-balanced between groups. Among the TM subgroup, adherence was very high (< 1% of missed biometric daily transmissions). During 6 months of follow-up, mean total healthcare cost per patient was 7949 euros (€) in the UC cohort and 4383 € in the TM cohort (relative risk reduction [RRR] of 45%; between-group differences 3546 €, 95% confidence interval 627-6464 €, bootstrap achieved significance level 0.022). This result was consistent across several subgroups (age, sex, frailty, left ventricular ejection fraction, educational level) and mainly driven by a 63% RRR in costs of hospitalization (which accounted for two-thirds of the total costs) in the TM cohort. 20 patients required HF admissions in the TM group, compared to 51 patients in the TM group (p < 0.001). This was associated with a 59% RRR in the need for diagnostic procedures. Total number of days of hospitalization per patient decreased from 12.2 in the UC group to 4.2 days in the TM group (p = 0.003), impelled by a reduction in HF and cardiovascular days of hospital stay. On the contrary, ambulatory care costs increase two-fold in the TM group compared to UC group. Nevertheless, these costs only accounted for a 19% of the total costs. Cost of devices needed to implement TM supposed 698 € per patient. Use of TM was associated with a 355 € increase in the cost of ambulatory neurohormonal treatment for HF. Conclusions TM implementation during the vulnerable phase after an acute HF admission was associated to a 45% RR reduction in mean total healthcare cost per patient. This result was driven by an almost two-thirds decrease in costs of hospitalization.UC and TM total healthcare costs

Transformational leadership and moral norms: green human resource management and behaviour

PurposeThis study introduces a theoretical model that integrates the conservation of resources (COR) and ability-motivation-opportunity (AMO) theories. This model is used to examine the relationship between green transformational leadership and green human resources management. Additionally, the study explores the influence of this relationship on employee green behaviour and the development of personal moral norms.Design/methodology/approachThe hypotheses developed in this study were tested through a quantitative approach, employing structural equation modelling via partial least squares (PLS-SEM) along with bootstrapping estimation techniques. For data analysis, the study utilized responses from a total of 475 participants.FindingsThe findings of this study indicate that the implementation of green transformational leadership has a positive impact on green human resources management. In turn, enhanced green human resources management positively influences employee green behaviour. The research further reveals that employee green behaviour not only contributes positively to the development of personal moral norms but also plays a crucial mediating role in the relationship between green human resources management and personal moral norms. Additionally, evidence supports the significant mediating effect of green human resources management in the relationship between green transformational leadership and employee green behaviour.Originality/valueThis study highlights the pivotal role of employee green behaviour as a foundational element in developing personal moral norms, marking a departure from prior research in the green transformational leadership field, which has predominantly focused on green human resources management. By implementing green transformational leadership strategies, organisations can not only enhance their competitive edge but also effectively attract highly skilled and qualified candidates, thereby making a significant contribution to the field.

Exploring the role of help‐seeking behavior, family financial socialization, and capability on financial well‐being

AbstractObjectiveThis study develops a conceptual framework for examining the associations between family financial socialization and financial capability on individuals' financial well‐being and explores the mediating role of financial help‐seeking when determining financial well‐being.BackgroundIndividuals' family financial socialization and financial capability are associated with their financial well‐being. This study extends the literature by examining whether seeking financial help can indirectly mediate the association between these factors and individuals' financial well‐being.MethodThis study used the 2016 National Financial Well‐Being Survey data set, consisting of 6,394 adults, and employed structural equation modeling with full information maximum likelihood and bootstrap estimation for empirical analyses.ResultsFinancial knowledge and perceived financial capability were positive contributors to financial well‐being. Family financial socialization showed positive direct and mixed indirect effects on help‐seeking behaviors. When mediated by help‐seeking behaviors, family financial socialization had significantly positive total and indirect associations and a negative direct association with financial well‐being.ConclusionThis study found that financial help‐seeking behavior was a significant factor in the indirect association between family financial socialization, financial knowledge, and well‐being.ImplicationsProviding access to professional advice and encouraging family finance discussions can enhance people's financial well‐being.

Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans Following Omega-3 Fatty Acid Supplementation.

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in response to n-3 HUFA supplementation. The current observational study aimed to simulate randomized clinical trial (RCT) conditions by matching 3766 AfAm and 15,553 non-Hispanic White (NHW) individuals from the VITAL trial utilizing propensity score matching to address the limitations related to differences in confounding variables between the two groups. Within matched groups (3766 AfAm and 3766 NHW), n-3 HUFA supplementation's impact on myocardial infarction (MI), stroke, and cardiovascular disease (CVD) mortality was assessed. A weighted decision tree analysis revealed belonging to the n-3 supplementation group as the most significant predictor of MI among AfAm but not NHW. Further logistic regression using the LASSO method and bootstrap estimation of standard errors indicated n-3 supplementation significantly lowered MI risk in AfAm (OR 0.17, 95% CI [0.048, 0.60]), with no such effect in NHW. This study underscores the critical need for future RCT to explore racial disparities in MI risk associated with n-3 HUFA supplementation and highlights potential causal differences between supplementation health outcomes in AfAm versus NHW populations.

Bootstrap Procedure for Correlation Model of Random Design Under Strong Dependence

This paper investigates the validity of a bootstrap least square estimate of a polynomial correlation model whose error terms are an autoregressive fractionally integrated moving average ARFIMA (p,d,q) strongly dependent time series. For an (r + 1)*1 vector B of unknown parameters, ^Ba an 'adjusted' least square estimate of B, ^B* a bootstrap estimate of B, it is shown that the distribution of ✓n(^ B*- ^Ba) converges to that of ✓n (^Ba - B), where n is the sample size. The result in this paper extends the correlation part of the results obtained by [9] and [6] to the case where the error term exhibits a long memory time series.

What Contributes to the Greening of the Polish Energy Industry? Pro-Environmental Inclusive Leadership and Pro-Environmental Behavior—A Moderation–Mediation Analysis

The functioning of the energy sector in Poland has been changing over recent years, influenced by growing concern for the environment. The concept of inclusive leadership can play a key role in the emergence of behavioral changes related to the development of environmental awareness and the initiation of pro-environmental attitudes and behaviors by employees. The guiding objective of this study is to verify a moderated mediation model of the impact of pro-environmental consciousness (PEC) on the mediating role of the fulfillment of the psychological contract for the environment (PCFE) in pro-environmental inclusive leadership (PEIL) and pro-environmental work behavior (PEWB) relationships in the energy industry. By self-completing a survey questionnaire using the computer-assisted web interview (CAWI) method, responses were collected from 649 respondents employed in various energy industry organizations. Structural equation modelling based on maximum likelihood with bootstrap estimation was used to test the hypothesis model. The findings confirm the assumptions of the research model, demonstrating a direct relationship between pro-environmental inclusive leadership and pro-environmental work behavior. Additionally, a mediating condition was found, where the fulfillment of the psychological contract for the environment mediates the relationship between pro-environmental inclusive leadership and pro-environmental work behavior, and pro-environmental consciousness moderates this process. It was found that higher pro-environmental consciousness enhances the indirect effect of pro-environmental inclusive leadership on employees’ pro-environmental work behavior through the fulfillment of the psychological contract for the environment. This highlights the importance of the fulfillment of the psychological contract for the environment and a pro-environmental consciousness in the effectiveness of pro-environmental leadership. The analysis indicates that managers who apply a pro-environmental inclusive management style contribute to an increasingly pro-environmental consciousness and the promotion pro-environmental work behavior among employees. Furthermore, the indirect effect of green leadership on employees’ green actions, through the fulfillment of a psychological contract for the environment that is moderated by their pro-environmental consciousness, highlights the importance of environmentally conscious leaders in building effective employee–employer relationships. These relationships facilitate the implementation of initiatives that accelerate the pace of change and enhance efforts to build tomorrow’s modern energy industry based on low- and zero-carbon energy sources. Additionally, this is one of the first studies in the Polish energy industry to provide insight into the role and importance of green leadership and green behavior in the energy industry.

Sensitivity and reliability comparisons of EWMA mean control chart based on robust scale estimators under non‐normal process: COVID data application

AbstractRobust control charts are getting vital importance in statistical process control theory as they are insensitive to the departure from normality. Therefore, the main objective of current work is to determine the effects of non‐normal process on the Exponentially Weighted Moving Average (EWMA) control chart. To achieve this goal, the sensitivity and reliability comparisons are made under the non‐normal process by comparing five robust M‐scale estimators, suggested in literature to modify the EWMA control limits for monitoring process mean and on the basis of percentile bootstrap estimator. The paper addresses the run length (RL) distribution of a robust EWMA control chart under the non‐normal process for which the exponential distribution is used as non‐normal process. The standard deviation of RL, out‐of‐control average run length (ARL), and shift detection probabilities are examined to assess the sensitivity and reliability of robust EWMA control charts for mean of monitoring process. The results of this research indicate that the classical EWMA control chart's performance is substantially impacted by the non‐normal distribution and the proposed EWMA control charts show higher sensitivity than classical one in terms of having smaller values of out‐of‐control ARLs. A real‐life example from the medical sciences field is provided the practical usage of the proposed control charts. The simulation analysis and practical example have shown that the suggested control charts are effective in quickly monitoring the out‐of‐control process.

The impact of psychological capital, cognitive diversity and temporal ambidexterity on adaptive performance through innovative work behaviour among IT employees in India

Purpose This study aims to examine how the Information Technology (IT) sector in India is changing sporadically to be relevant to be able to meet the changing demands. Companies are striving hard to be able to leverage any such factor that adds to the competitive advantage needed to survive in this dynamic market. Therefore, an organization has to be able to keep innovation as its primary objective. Design/methodology/approach This study used a cross-sectional research design and the data from 303 IT professionals were used to validate the relationships among psychological capital (PSC), cognitive diversity (COD), temporal ambidexterity (TEA), innovative work behaviour (IWB) and adaptive performance (ADP). Hypotheses testing was done through the SEM and mediation analysis was conducted using bootstrap estimation in SPSS AMOS. Findings The results suggest that PSC significantly affects both IWB and ADP. COD has a significant effect only on IWB. In addition, directly impacts ADP. Mediation analysis indicates that COD has full mediation between the relationship of IWB and ADP, whereas PSC has a partial mediation. However, TEA has no mediation indicating that an employee needs to have support from his organization to be ambidextrous. Originality/value This study shows how significant innovation and IWBs are in the context of the IT sector and how positive thinking, a diverse set of people and the balance between short-term and long-term goals could promote IWBs in an individual leading to better ADP.

Exon-Skipping–Based Subtyping of Colorectal Cancers

The identification of colorectal cancer (CRC) molecular subtypes has prognostic and potentially diagnostic value for patients, yet reliable subtyping remains unavailable in the clinic. The current consensus molecular subtype (CMS) classification in CRCs is based on complex RNA expression patterns quantified at the gene level. The clinical application of these methods, however, is challenging due to high uncertainty of single-sample classification and associated costs. Alternative splicing, which strongly contributes to transcriptome diversity, has rarely been used for tissue type classification. Here, we present an AS-based CRC subtyping framework sensitive to differential exon use that can be adapted for clinical application. Unsupervised clustering was used to measure the strength of association between different categories of alternative splicing and CMS. To build a classifier, the ground truth for CMS labels was derived from expression data quantified at the gene level. Feature selection was achieved through bootstrapping and L1-penalized estimation. The resulting feature space was used to construct a subtype prediction framework applicable to single and multiple samples. The performance of the models was evaluated on unseen CRCs from 2 independent sources (Indivumed, n= 129; The Cancer Genome Atlas, n= 99). We developed a CRC subtype identifier based on 29 exon-skipping events that accurately classifies unseen tumors and enables more precise differentiation of subtypes characterized by distinct biological and prognostic features as compared to classifiers based on gene expression. Here, we demonstrate that a small number of exon-skipping events can reliably classify CRC subtypes using individual patient specimens in a manner suitable to clinical application.

Bootstrap Approximation of Model Selection Probabilities for Multimodel Inference Frameworks.

Most statistical modeling applications involve the consideration of a candidate collection of models based on various sets of explanatory variables. The candidate models may also differ in terms of the structural formulations for the systematic component and the posited probability distributions for the random component. A common practice is to use an information criterion to select a model from the collection that provides an optimal balance between fidelity to the data and parsimony. The analyst then typically proceeds as if the chosen model was the only model ever considered. However, such a practice fails to account for the variability inherent in the model selection process, which can lead to inappropriate inferential results and conclusions. In recent years, inferential methods have been proposed for multimodel frameworks that attempt to provide an appropriate accounting of modeling uncertainty. In the frequentist paradigm, such methods should ideally involve model selection probabilities, i.e., the relative frequencies of selection for each candidate model based on repeated sampling. Model selection probabilities can be conveniently approximated through bootstrapping. When the Akaike information criterion is employed, Akaike weights are also commonly used as a surrogate for selection probabilities. In this work, we show that the conventional bootstrap approach for approximating model selection probabilities is impacted by bias. We propose a simple correction to adjust for this bias. We also argue that Akaike weights do not provide adequate approximations for selection probabilities, although they do provide a crude gauge of model plausibility.

Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction.

Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

Quantitative limit theorems and bootstrap approximations for empirical spectral projectors

Given finite i.i.d. samples in a Hilbert space with zero mean and trace-class covariance operator Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma $$\\end{document}, the problem of recovering the spectral projectors of Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma $$\\end{document} naturally arises in many applications. In this paper, we consider the problem of finding distributional approximations of the spectral projectors of the empirical covariance operator Σ^\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\hat{\\Sigma }}$$\\end{document}, and offer a dimension-free framework where the complexity is characterized by the so-called relative rank of Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma $$\\end{document}. In this setting, novel quantitative limit theorems and bootstrap approximations are presented subject to mild conditions in terms of moments and spectral decay. In many cases, these even improve upon existing results in a Gaussian setting.

Inference on Weibull inverted exponential distribution under progressive first-failure censoring with constant-stress partially accelerated life test

Accelerated life tests (ALTs) play a pivotal role in life testing experiments as they significantly reduce costs and testing time. Hence, this paper investigates the statistical inference issue for the Weibull inverted exponential distribution (WIED) under the progressive first-failure censoring (PFFC) data with the constant-stress partially ALT (CSPALT) under progressive first-failure censoring (PFFC) data for Weibull inverted exponential distribution (WIED). For classical inference, maximum likelihood (ML) estimates for both the parameters and the acceleration factor are derived. Making use of the Fisher information matrix (FIM), asymptotic confidence intervals (ACIs) are constructed for all parameters. Besides, two parametric bootstrap techniques are implemented. For Bayesian inference based on a proposed technique for eliciting the hyperparameters, the Markov chain Monte Carlo (MCMC) technique is provided to acquire Bayesian estimates. In this context, the Bayesian estimates are obtained under symmetric and asymmetric loss functions, and the corresponding credible intervals (CRIs) are constructed. A simulation study is carried out to assay the performance of the ML, bootstrap, and Bayesian estimates, as well as to compare the performance of the corresponding confidence intervals (CIs). Finally, real-life engineering data is analyzed for illustrative purposes.

Tests for almost stochastic dominance

We introduce a 2-dimensional stochastic dominance (2DSD) index to characterize both strict and almost stochastic dominance. Based on this index, we derive an estimator for the minimum violation ratio (MVR), also known as the critical parameter, of the almost stochastic ordering condition between two variables. We determine the asymptotic properties of the empirical 2DSD index and MVR for the most frequently used stochastic orders. We also provide conditions under which the bootstrap estimators of these quantities are strongly consistent. As an application, we develop consistent bootstrap testing procedures for almost stochastic dominance. The performance of the tests is checked via simulations and the analysis of real data.

Dynamics of deposit dollarization in Turkey

<p style="text-align:justify"><span style="font-size:16px"><span style="font-family:Times New Roman,Times,serif"><span dir="ltr" lang="EN-US"><span style="color:black">Deposit dollarization in Turkey has been on the rise, reaching record levels in 2022. This was caused by the worsening macroeconomic fundamentals along with the transition to the presidential system in 2018 and the associated deviation from the monetary policy stance afterward. The unique case of Turkey presents an excellent natural experiment to explore the dynamics of deposit dollarization and how it is affected by the changes in the institutional structure as well as various social, economic, and financial shocks in a relatively short time. This study examines the time varying reactions of deposit dollarization for the period between 2013 and 2022 based on rolling window maximum entropy bootstrap estimates. Our findings reveal that all the model variables had significant and sometimes asymmetric effects on deposit dollarization during the different stages of this turbulent period. In particular, we observe diminishing effects of real exchange rate, policy credibility and consumer confidence over time. The findings provide ample new evidence regarding the dynamics of deposit dollarization in general, and how it is affected by loss of monetary policy credibility.</span></span></span></span></p>

Bias-Correction Methods for the Unit Exponential Distribution and Applications

The bias of the maximum likelihood estimator can cause a considerable estimation error if the sample size is small. To reduce the bias of the maximum likelihood estimator under the small sample situation, the maximum likelihood and parametric bootstrap bias-correction methods are proposed in this study to obtain more reliable maximum likelihood estimators of the unit exponential distribution parameters. The procedure to implement the bias-corrected maximum likelihood estimation method is derived analytically, and the steps to obtain the bias-corrected bootstrap estimators are presented. The simulation results show that the proposed maximum likelihood bootstrap bias-correction method can significantly reduce the bias and mean squared error of the maximum likelihood estimators for most of the parameter combinations in the simulation study. A soil moisture data set and a numerical example are used for illustration.

Servant leadership’s impact on trust, commitment, and performance in higher education

<span lang="EN-US">This research examined the relationship between servant leadership (SL), organizational trust (OT), organizational commitment (OC), and task performance (TP). It employed a quantitative research design with partial least squares-structural equation modeling (PLS-SEM) and bootstrap estimation. Data were collected through a survey of 111 lecturers from 12 universities in Eastern Indonesia. It found that SL does not affect TP and OC. Meanwhile, OT affects OC but not TP. Further, OC does not affect TP. Therefore, OT does not affect TP through OC. Likewise, SL does not affect TP through OC. It practically implies that universities should be concerned the selection and placement of professional leaders. Successful professional leaders support and enhance the OC and TP of lecturers with SL. This study enriches leadership literature empirically examining SL, TP, OT, and OC in religious higher education. Besides, it provides evidence that SL in tertiary institutions does not exhibit a positive impact on lecturer performance. Institutional limitations lead to difficult situations and allow lecturers to accept reality. Furthermore, it uncovers challenges and demands the need for in-depth investigation of SL studies, which are paradoxical and contradictory to the previous SL theories and findings.</span>

Clinically relevant estimation of minimal number of trials for the uncontrolled manifold analysis

Movement biomarkers are crucial for assessing sensorimotor impairments and tracking the effects of interventions over time. The Uncontrolled Manifold (UCM) analysis has been proposed as a novel biomarker for evaluating movement stability and coordination in various motor tasks across neurological and musculoskeletal disorders. Through inter-trial analysis, the UCM partitions the variance of elemental variables (e.g., finger forces) into components that affect (VORT) and do not affect (VUCM) a performance variable (e.g., total force). A third index, ΔV, is computed as the normalized difference between VORT and VUCM. However, the minimum number of trials required to achieve stable UCM estimates, considering its clinimetric properties, is unknown. This study aimed to determine the minimal number (N) of trials for UCM estimates by computing bootstrap estimates of standard errors (SE) at different N trials using thresholds based on the minimal detectable change (MDC, i.e., the minimum change in an outcome measure beyond measurement error). Thirteen adults (24.6 ± 1.1 years old) performed a finger-pressing coordination task. We computed the 95 % confidence intervals (CI) of bootstrap SE distributions for each UCM estimate and detected the lowest number of trials with the 95 % CI of SE below each MDC threshold. We found the minimal N of trials required was VUCM = 14, VORT = 4 and ΔV = 18. Our findings highlight that a relatively low number of trials (i.e., N = 18) are sufficient to compute all UCM estimates beyond the MDC, supporting the use of the UCM framework in clinical settings where many repetitions of a motor task are not practical.

Identifying Heterogeneity in SAR Data with New Test Statistics

This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called “homogeneous” or “textureless” regions. The GI0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and GI0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of p-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data.

Averaging Estimation for Instrumental Variables Quantile Regression

AbstractThis paper proposes two averaging estimation methods to improve the finite‐sample efficiency of the instrumental variables quantile regression (IVQR) estimator. I propose using the usual quantile regression for averaging to take advantage of cases when endogeneity is not too strong. I also propose using two‐stage least squares to take advantage of cases when heterogeneity is not too strong. The first averaging method is to apply a recent proposal for GMM averaging to the IVQR model based on this proposed intuition. My implementation involves many computational considerations and builds on recent developments in the quantile literature. The second averaging method is a new bootstrap model averaging method that directly averages among IVQR, quantile regression, and two‐stage least squares estimators. More specifically, I find the optimal weights from bootstrapped samples and then apply the bootstrap‐optimal weights to the original sample. The bootstrap method is simpler to compute and generally performs better in simulations, but uniform dominance results have not been formally proved. Simulation results demonstrate that in the multiple‐regressors/instruments case, both the GMM averaging and bootstrap estimators have uniformly smaller risk than the IVQR estimator across data‐generating processes with a variety of combinations of different endogeneity levels and heterogeneity levels.