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AbstractFrequently, the covariates in the linear predictors of a zero-inflated Poisson regression model are prone to missingness. When the missing data process is at random, the naive estimator and the inverse probability weighted estimator of the model parameter are known to be biased or less efficient. Hence, we propose four different semiparametric double robust weighting M-estimators of a zero-inflated Poisson model. In particular, these estimators differ in term of the nature and the combination of the nuisance components in the estimating equation. The Nadaraya kernel and the generalized additive models are the two smoothing approaches used to estimate the nuisance parameters in step 1. Each estimator in step 2 is a solution to a specific M-estimating equation which requires two distinct nonparametric nuisance components including the selection probability and the augmentation term. The resulting semiparametric weighted M-estimators are double robust and relatively more efficient than their parametric counterpart and related inverse probability weighted estimators. We examine the performance of the proposed estimators by conducting a simulation study and exploring a road traffic dataset. Overall, the performance of the proposed estimators is satisfactory and potential for various extensions. [ABSTRACT FROM AUTHOR]

This paper extends the Bayesian semiparametric stochastic volatility (SV‐DPM) model. Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density beyond that attributed to parametric latent volatility. The new model nests several special cases as well as the SV‐DPM. We also discuss posterior and predictive density simulation methods for the model. Applied to equity returns, foreign exchange rates, oil price growth and industrial production growth, the new model improves density forecasts, compared with the SV‐DPM, a stochastic volatility with Student's t innovations and other fat‐tailed volatility models. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality. Design/methodology/approach – To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator. Findings – In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator. Originality/value – A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.

We propose a semiparametric method to estimate average treatment effects in observational studies based on the assumption of unconfoundedness. Assume that the propensity score model and outcome model are a general single index model, which are estimated by the kernel method and the unknown index parameter is estimated via linearized maximum rank correlation method. The proposed estimator is computationally tractable, allows for large dimension covariates and not involves the approximation of link functions. We showed that the proposed estimator is consistent and asymptotically normally distributed. In general, the proposed estimator is superior to existing methods when the model is incorrectly specified. We also provide an empirical analysis on the average treatment effect and average treatment effect on the treated of 401(k) eligibility on net financial assets. [ABSTRACT FROM AUTHOR]

We examine the role and relationship of global value chains and inventories by delving into the dynamic effects of upstream manufacturing shocks on downstream retailer performance. Motivated by the pivotal role inventories play in firm demand management, our research employs a novel two-step methodology involving a reduced-form semiparametric smooth coefficient model, and a structural vector autoregressive model. The findings, based on monthly data spanning from January 1999 to December 2021, reveal a profound, and enduring impact of manufacturing supply chain shocks on the retail sector. Following a unit supply chain shock, downstream retailers experience a substantial and lasting increase in inventory accumulation, accompanied by a short-term decline, and subsequent stabilization in sales. Moreover, postshock, retailers experience a permanent decrease in output, underscoring the far-reaching, and persistent consequences of disruptions in upstream supply chain agents on downstream retail operations. [ABSTRACT FROM AUTHOR]

We propose an asset pricing factor model constructed with semiparametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-splines coefficients, with the strengthened power approaching one asymptotically. We also investigate the structure of mispricing components through Hierarchical K-means Clusterings. We apply our methodology to CRSP (Center for Research in Security Prices) and Compustat data for the U.S. stock market with one-year rolling windows during 1967–2017. This empirical study shows the presence of mispricing functions in certain time blocks. We also find that distinct clusters of the same characteristics lead to similar arbitrage returns, forming a "peer group" of arbitrage characteristics. [ABSTRACT FROM AUTHOR]

This paper investigates variable selection in semiparametric spatial autoregressive panel data model with random effects. A penalized profile maximum-likelihood method is proposed with adaptive lasso penalty which achieves parameter estimation and variable selection at the same time. Under some regular conditions, we prove the theoretical properties of the estimators, including consistency and oracle property. In addition, we develop a feasible logarithm and carry out numerical simulations to examine the finite sample performance of this method. At last, a real data study about the investment influencing factors of the "Belt and Road" initiative is presented for illustration purpose. [ABSTRACT FROM AUTHOR]

AbstractThere is a wide range of applications where the local extrema of a function are the key quantity of interest. However, there is surprisingly little work on methods to infer local extrema with uncertainty quantification in the presence of noise. By viewing the function as an infinite-dimensional nuisance parameter, a semiparametric formulation of this problem poses daunting challenges, both methodologically and theoretically, as (i) the number of local extrema may be unknown, and (ii) the induced shape constraints associated with local extrema are highly irregular. In this article, we build upon a derivative-constrained Gaussian process prior recently proposed by Yu et al. to derive what we call an encompassing approach that indexes possibly multiple local extrema by a single parameter. We provide closed-form characterization of the posterior distribution and study its large sample behavior under this unconventional encompassing regime. We show that the posterior measure converges to a mixture of Gaussians with the number of components matching the underlying truth, leading to posterior exploration that accounts for multi-modality. Point and interval estimates of local extrema with frequentist properties are also provided. The encompassing approach leads to a remarkably simple, fast semiparametric approach for inference on local extrema. We illustrate the method through simulations and a real data application to event-related potential analysis. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile regression coefficients are a solution. Identification is obtained when this system of equations has a unique solution. Our estimation procedure solves an empirical analogue of the system of equations. We derive conditions under which the estimator is asymptotically normal and prove the validity of a bootstrap procedure for inference. The finite sample performance of the approach is evaluated through numerical simulations. An application to the national Job Training Partnership Act study illustrates the method. [ABSTRACT FROM AUTHOR]

We consider theoretical and practical issues for innovatively using a large number of covariates in clinical trials to achieve various design objectives without model misspecification. Specifically, we propose a new family of semiparametric covariate‐adjusted response‐adaptive randomization (CARA) designs and we use the target maximum likelihood estimation (TMLE) to analyze the correlated data from CARA designs. Our approach can flexibly achieve multiple objectives and correctly incorporate the effect of a large number of covariates on the responses without model misspecification. We also obtain the consistency and asymptotic normality of the target parameters, allocation probabilities, and allocation proportions. Numerical studies demonstrate that our approach has advantages over existing approaches, even when the data‐generating distribution is complicated. [ABSTRACT FROM AUTHOR]

We propose an extension of Spearman's correlation for censored continuous and discrete data that permits covariate adjustment. Previously proposed nonparametric and semiparametric Spearman's correlation estimators require either nonparametric estimation of the bivariate survival surface or parametric assumptions about the dependence structure. In practice, nonparametric estimation of the bivariate survival surface is difficult, and parametric assumptions about the correlation structure may not be satisfied. Therefore, we propose a method that requires neither and uses only the marginal survival distributions. Our method estimates the correlation of probability‐scale residuals, which has been shown to equal Spearman's correlation when there is no censoring. Because this method relies only on marginal distributions, it tends to be less variable than the previously suggested nonparametric estimators, and the confidence intervals are easily constructed. Although under censoring, it is biased for Spearman's correlation as our simulations show, it performs well under moderate censoring, with a smaller mean squared error than nonparametric approaches. We also extend it to partial (adjusted), conditional, and partial‐conditional correlation, which makes it particularly relevant for practical applications. We apply our method to estimate the correlation between time to viral failure and time to regimen change in a multisite cohort of persons living with HIV in Latin America. [ABSTRACT FROM AUTHOR]

In developing countries, water conservation practices in agriculture are promoted to overcome the adverse effects of water scarcity. The data obtained from in-person interview surveys of 385 Nepali farm households were analyzed using parametric, semiparametric, and nonparametric models to identify factors influencing water conservation practices. We find that both nonparametric and semiparametric models are more appropriate than a parametric model. The results indicated that off-farm income is negatively associated with the number of practices adopted in a nonparametric model. The results from a semiparametric model show that farmers who had food sufficiency to feed the family in the previous year, access to credit, and agriculture micro-irrigation infrastructure adopt more water conservation practices. These results indicate the potential benefits of formulating appropriate policies and programs to enhance farm production and agricultural income to encourage farmers to adopt water conservation practices for sustainable agriculture in Nepal. [ABSTRACT FROM AUTHOR]

The objective of this research is to develop a hedonic model for office space rent prices in Istanbul, using a data set consisting of 2348 office spaces. The data includes information on office building and office space characteristics, as well as features related to the lease, location, and neighborhood that impact office rents. To examine the connection between office rent and these factors, a semiparametric geoadditive model was utilized, which allows for flexibility in functional form and captures location effects through geographical smoothing functions instead of location dummies. The results indicate that the link between office rent determinants and office space rents is nonlinear. The vacancy rate, a measure of office space demand, is the determinant with the greatest impact on office rents. The second most important determinant of office rents is that the office space is in an A-class office building, and the third most influential factor is that the office spaces have a view of the Bosporus. [ABSTRACT FROM AUTHOR]

Multivariate "Bayesian" regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia. [ABSTRACT FROM AUTHOR]

The National Alzheimer's Coordinating Center Uniform Data Set includes test results from a battery of cognitive exams. Motivated by the need to model the cognitive ability of low‐performing patients we create a composite score from ten tests and propose to model this score using a partially linear quantile regression model for longitudinal studies with non‐ignorable dropouts. Quantile regression allows for modeling non‐central tendencies. The partially linear model accommodates nonlinear relationships between some of the covariates and cognitive ability. The data set includes patients that leave the study prior to the conclusion. Ignoring such dropouts will result in biased estimates if the probability of dropout depends on the response. To handle this challenge, we propose a weighted quantile regression estimator where the weights are inversely proportional to the estimated probability a subject remains in the study. We prove that this weighted estimator is a consistent and efficient estimator of both linear and nonlinear effects. [ABSTRACT FROM AUTHOR]

Quarantine length for individuals who have been at risk for infection with SARS‐CoV‐2 has been based on estimates of the incubation time distribution. The time of infection is often not known exactly, yielding data with an interval censored time origin. We give a detailed account of the data structure, likelihood formulation and assumptions usually made in the literature: (i) the risk of infection is assumed constant on the exposure window and (ii) the incubation time follows a specific parametric distribution. The impact of these assumptions remains unclear, especially for the right tail of the distribution which informs quarantine policy. We quantified bias in percentiles by means of simulation studies that mimic reality as close as possible. If assumption (i) is not correct, then median and upper percentiles are affected similarly, whereas misspecification of the parametric approach (ii) mainly affects upper percentiles. The latter may yield considerable bias. We suggest a semiparametric method that provides more robust estimates without the need of a parametric choice. Additionally, we used a simulation study to evaluate a method that has been suggested if all infection times are left censored. It assumes that the width of the interval from infection to latest possible exposure follows a uniform distribution. This assumption gave biased results in the exponential phase of an outbreak. Our application to open source data suggests that focus should be on the level of information in the observations, as expressed by the width of exposure windows, rather than the number of observations. [ABSTRACT FROM AUTHOR]

Stationary points embedded in the derivatives are often critical for a model to be interpretable and may be considered as key features of interest in many applications. We propose a semiparametric Bayesian model to efficiently infer the locations of stationary points of a nonparametric function, which also produces an estimate of the function. We use Gaussian processes as a flexible prior for the underlying function and impose derivative constraints to control the function's shape via conditioning. We develop an inferential strategy that intentionally restricts estimation to the case of at least one stationary point, bypassing possible mis‐specifications in the number of stationary points and avoiding the varying dimension problem that often brings in computational complexity. We illustrate the proposed methods using simulations and then apply the method to the estimation of event‐related potentials derived from electroencephalography (EEG) signals. We show how the proposed method automatically identifies characteristic components and their latencies at the individual level, which avoids the excessive averaging across subjects that is routinely done in the field to obtain smooth curves. By applying this approach to EEG data collected from younger and older adults during a speech perception task, we are able to demonstrate how the time course of speech perception processes changes with age. [ABSTRACT FROM AUTHOR]

Featured Application: In the present work, the spatiotemporal dynamics of kinetic wind energy is analyzed with and without allowance being made for the kinetic energy of the outliers retrieved by the postprocessing of the minisodar measurements of three wind velocity components and their variances. The wind outliers are taken to mean wind velocities, including wind gusts, the distribution of which deviates from the prior distribution of the majority of observations. The minisodar data were processed using robust parametric, semiparametric, and semi-nonparametric algorithms developed by the authors. Allowing for the contribution of the wind outliers in the parametric estimates of the kinetic wind energy enabled its fine structure to be determined, along with an estimation of its effect on the landing and takeoff of airplanes, light flying objects, high-rise buildings, and bridges, and an evaluation of the energy potentials of wind turbines. In the present work, we analyze the spatiotemporal dynamics of the kinetic wind energy with and without allowance for the kinetic energy of outliers. We first separated the contributions of the mean kinetic energy and the kinetic energy of the outliers and estimated the latter using robust parametric, semiparametric, and semi-nonparametric algorithms developed by the authors. The kinetic wind energy was estimated by the postprocessing of minisodar measurements of three wind velocity components and their variances in the lower 200 m layer of the atmosphere. By the outliers, we mean wind velocities, including wind gusts, the distribution of which deviates from the prior distribution of the majority of observations. A nonmonotonic increase in the kinetic energy of the outliers with sounding altitude was established. Physically, this can be explained by a nonmonotonic increase in the turbulent kinetic energy of local air vortices in the atmospheric boundary layer (ABL). The vertical extension of the outlier layers was of the order of 10–20 m. [ABSTRACT FROM AUTHOR]

Abstract Volatility is one of the most important features of financial time series data. There are often structural changes in volatility over time, and an accurate estimation of the volatility of financial time series requires careful identification of change-points. A common approach to modeling the volatility of time series data is based on the well-known GARCH model. Although the problem of change-point estimation of volatility dynamics derived from the GARCH model has been considered in the literature, these approaches rely on parametric assumptions of the conditional error distribution, which are often violated in financial time series. This may lead to inaccuracies in change-point detection, resulting in unreliable GARCH volatility estimates. This article introduces a novel change-point detection algorithm based on a semiparametric GARCH model. The proposed method retains the structural advantages of the GARCH process while incorporating the flexibility of nonparametric conditional error distribution. The approach utilizes a penalized likelihood derived from a semiparametric GARCH model and an efficient binary segmentation algorithm. The results show that in terms of change-point estimation and detection accuracy, the semiparametric method outperforms the commonly used Quasi-MLE (QMLE) and other variations of the GARCH models in wide-ranging scenarios. [ABSTRACT FROM AUTHOR]

Aalen's linear hazard rate regression model is a useful and increasingly popular alternative to Cox' multiplicative hazard rate model. It postulates that an individual has hazard rate function h (s) = z 1 α 1 (s) + ⋯ + z r α r (s) in terms of his covariate values z 1 , ... , z r . These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions α j (s) are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model's parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study. [ABSTRACT FROM AUTHOR]

In this article, we study a semiparametric multiplicative volatility model, which splits up into a nonparametric part and a parametric GARCH component. The nonparametric part is modeled as a product of a deterministic time trend component and of further components that depend on stochastic regressors. We propose a two-step procedure to estimate the model. To estimate the nonparametric components, we transform the model and apply a backfitting procedure. The GARCH parameters are estimated in a second step via quasi maximum likelihood. We show consistency and asymptotic normality of our estimators. Our results are obtained using mixing properties and local stationarity. We illustrate our method using financial data. Finally, a small simulation study illustrates a substantial bias in the GARCH parameter estimates when omitting the stochastic regressors. [ABSTRACT FROM AUTHOR]

Leptospirosis, the infectious disease caused by a spirochete bacteria, is a major public health problem worldwide. In Argentina, some regions have climatic and geographical characteristics that favor the habitat of bacteria of the Leptospira genus, whose survival strongly depends on climatic factors, enhanced by climate change, which increase the problems associated with people's health. In order to have a method to predict leptospirosis cases, in this paper, five time series forecasting methods are compared: two parametric (autoregressive integrated moving average and an alternative one that allows covariates, ARIMA and ARIMAX, respectively), two nonparametric (Nadaraya-Watson Kernel estimator, one and two kernels versions, NW-1 K and NW-2 K), and one semiparametric (semi-functional partial linear regression, SFPLR) method. For this, the number of cases of leptospirosis registered from 2009 to 2020 in three important cities of northeastern Argentina is used, as well as hydroclimatic covariates related to the presence of cases. According to the obtained results, there is no method that improves considerably the rest and can be recommended as a unique tool for leptospirosis prediction. However, in general, the NW-2 K method gets a better performance. This work, in addition to using a long-term high-quality time series, enriches the area of applications of statistical models to epidemiological leptospirosis data by the incorporation of hydroclimatic variables, and it is recommended directing further efforts in this line of research, under the context of current climate change. [ABSTRACT FROM AUTHOR]

Regression analysis is known as statistical methods applied to model and analyze the relationship between variables. Regression method can be examined as parametric, non-parametric and semiparametric regression methods. The parametric regression method assumes that the dependent variable is in a linear relationship with the independent variables and that the shape of the relationship is known. If these assumptions are not met, non-parametric regression methods are applied. However, these methods cause difficulties especially in the interpretation part due to the problem of multidimensionality when there is more than one independent variable. Thus, when there is more than one independent variable, some of the independent variables may be in a linear relationship with the dependent variable, while the other part may be in a nonlinear relationship. Thus, in order to model these relationships, semiparametric regression methods, which are the additive combination of parametric and non-parametric regression methods, are used. In this study, parametric regression method, definition of non-parametric regression method and assumption conditions are given. It has been shown that the semiparametric regression method can be applied in cases where these assumptions are not met. Thus, in the study, regression methods were examined in three different parts, and parametric, non-parametric and semiparametric regression methods were examined theoretically. [ABSTRACT FROM AUTHOR]

Marginal structural models for time-fixed treatments fit using inverse-probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite-sample bias when data are sparse, as is typical for large-sample procedures. Here we propose a semi-Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite-sample performance. This approach uses simple symmetric data-augmentation priors. Limited simulation experiments indicate that the proposed approach reduces finite-sample bias and improves confidence-interval coverage when the true values lie within the central "hill" of the prior distribution. We illustrate the approach with data from a nonexperimental study of HIV treatments.

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval‐censored survival data under a paradigm of Bayesian ensemble learning, called soft Bayesian additive regression trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left‐censored, right‐censored, and interval‐censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high‐dimensional data with complex underlying associations. [ABSTRACT FROM AUTHOR]

The number of new infections per day is a key quantity for effective epidemic management. It can be estimated relatively directly by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen‐effective reproduction number, R, using data gathered from the clinical response to the disease. For coronavirus disease 2019 (Covid‐19/SARS‐Cov‐2), such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty, it is useful to retrospectively reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on first‐wave Covid‐19 deaths and the disease duration distribution suggests that fatal infections were in decline before full UK lockdown (24 March 2020), and that fatal infections in Sweden started to decline only a day or two later. An analysis of UK data using the model of Flaxman et al. gives the same result under relaxation of its prior assumptions on R, suggesting an enhanced role for non‐pharmaceutical interventions short of full lockdown in the UK context. Similar patterns appear to have occurred in the subsequent two lockdowns. [ABSTRACT FROM AUTHOR]

The accuracy of a diagnostic test is often expressed using a pair of measures: sensitivity (proportion of test positives among all individuals with target condition) and specificity (proportion of test negatives among all individuals without target condition). If the outcome of a diagnostic test is binary, results from different studies can easily be summarized in a meta‐analysis. However, if the diagnostic test is based on a discrete or continuous measure (e.g., a biomarker), several cut‐offs within one study as well as among different studies are published. Instead of taking all information of the cut‐offs into account in the meta‐analysis, a single cut‐off per study is often selected arbitrarily for the analysis, even though there are statistical methods for the incorporation of several cut‐offs. For these methods, distributional assumptions have to be met and/or the models may not converge when specific data structures occur. We propose a semiparametric approach to overcome both problems. Our simulation study shows that the diagnostic accuracy is under‐estimated, although this underestimation in sensitivity and specificity is relatively small. The comparative approach of Steinhauser et al. is better in terms of coverage probability, but may lead to convergence problems. In addition to the simulation results, we illustrate the application of the semiparametric approach using a published meta‐analysis for a diagnostic test differentiating between bacterial and viral meningitis in children. [ABSTRACT FROM AUTHOR]

In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution. The module is an extension to the open-source program JAGS by which a Gibbs sampler can be applied without requiring the derivation of complete conditionals and the subsequent implementation of strategies to draw samples from unknown distributions. The PE distribution is widely used in the fields of survival analysis and reliability. Currently, it can only be implemented in JAGS through methods to indirectly specify the likelihood based on the Poisson or Bernoulli probabilities. Our module provides a more straightforward implementation and is thus more attractive to the researchers aiming to spend more time exploring the results from the Bayesian inference rather than implementing the Markov Chain Monte Carlo algorithm. For those interested in extending JAGS, this work can be seen as a tutorial including important information not well investigated or organized in other materials. Here, we describe how to use the module taking advantage of the interface between R and JAGS. A short simulation study is developed to ensure that the module behaves well and a real illustration, involving two PE models, exhibits a context where the module can be used in practice.

Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia.

In this article, based on cyclical monotonicity moment inequalities implied by the random utility choice model, we propose a computationally fast estimator for semiparametric multinomial choice models. The term semiparametric refers to the fact that we do not specify a particular functional form for the error term in the random utility function. The proposed estimators are consistent. Comparing with the estimators developed by the cyclic monotonicity method, simulations show the estimators we proposed have great advantages on the running time. [ABSTRACT FROM AUTHOR]

In the present work, we analyze the spatiotemporal dynamics of the kinetic wind energy with and without allowance for the kinetic energy of outliers. We first separated the contributions of the mean kinetic energy and the kinetic energy of the outliers and estimated the latter using robust parametric, semiparametric, and semi-nonparametric algorithms developed by the authors. The kinetic wind energy was estimated by the postprocessing of minisodar measurements of three wind velocity components and their variances in the lower 200 m layer of the atmosphere. By the outliers, we mean wind velocities, including wind gusts, the distribution of which deviates from the prior distribution of the majority of observations. A nonmonotonic increase in the kinetic energy of the outliers with sounding altitude was established. Physically, this can be explained by a nonmonotonic increase in the turbulent kinetic energy of local air vortices in the atmospheric boundary layer (ABL). The vertical extension of the outlier layers was of the order of 10–20 m.

Floods can be characterized by various correlated variables such as peak flow, flood volume, duration, and time to peak. Hence, flood risk can be better assessed by performing frequency analysis in a multivariate (rather than a univariate) framework. However, multivariate approaches involve sophisticated analyses that require considerably more data than univariate approaches. In a univariate framework, flood risk assessment at an ungauged or data‐sparse location/site is generally performed through regional frequency analysis (RFA), based on information pooled from a group of sites resembling the target site. However, RFA has received little attention in the multivariate framework. The available literature recommends the use of index‐flood approach (IFA) for multivariate RFA (MRFA), even though IFA has theoretical shortcomings. Another issue is that marginals of all the flood‐related variables may not exhibit extreme behavior simultaneously. Conventionally used at‐site multivariate models are not suitable for describing the dependence structure of extremes in the regions of the support of the joint distribution where only some variables exhibit extreme behavior. This article proposes a conditional extreme values approach (CEA) to address the aforementioned issues in MRFA. Its effectiveness is demonstrated through Monte Carlo simulation experiments and a case study on watersheds from a flood‐prone region in India. Results indicate that the proposed approach can reliably predict the joint distribution of multiple flood‐related variables (and thus flood risk) at ungauged/sparsely gauged sites by effectively capturing the regional dependence structure between those variables. Key Points: Proposes a novel approach to multivariate regional flood frequency analysis based on the conditional extreme values modelThe potential of the approach is demonstrated through simulation experiments and application on watersheds in a flood‐prone region of IndiaApproach effectively models the joint distribution of extremes in regions of the support where a subset of variables exhibits extreme behavior [ABSTRACT FROM AUTHOR]

We consider the problem of jointly modeling multiple covariance matrices by partial common principal component analysis (PCPCA), which assumes a proportion of eigenvectors to be shared across covariance matrices and the rest to be individual‐specific. This paper proposes consistent estimators of the shared eigenvectors in the PCPCA as the number of matrices or the number of samples to estimate each matrix goes to infinity. We prove such asymptotic results without making any assumptions on the ranks of eigenvalues that are associated with the shared eigenvectors. When the number of samples goes to infinity, our results do not require the data to be Gaussian distributed. Furthermore, this paper introduces a sequential testing procedure to identify the number of shared eigenvectors in the PCPCA. In simulation studies, our method shows higher accuracy in estimating the shared eigenvectors than competing methods. Applied to a motor‐task functional magnetic resonance imaging data set, our estimator identifies meaningful brain networks that are consistent with current scientific understandings of motor networks during a motor paradigm. [ABSTRACT FROM AUTHOR]

Background Length of stay is a significant indicator of care effectiveness and hospital performance. Owing to the limited number of healthcare centers and facilities, it is important to optimize length of stay and associated factors. Purpose The present study aimed to investigate factors associated with neonatal length of stay in the neonatal intensive care unit (NICU) using parametric and semiparametric models and compare model fitness according to Akaike information criterion (AIC) between 2016 and 2018. Methods This retrospective cohort study reviewed 600 medical records of infants admitted to the NICU of Bandar Abbas Hospital. Samples were identified using census sampling. Factors associated with NICU length of stay were investigated based on semiparametric Cox model and 4 parametric models including Weibull, exponential, log-logistic, and log-normal to determine the best fitted model. The data analysis was conducted using R software. The significance level was set at 0.05. Results The study findings suggest that breastfeeding, phototherapy, acute renal failure, presence of mechanical ventilation, and availability of central venous catheter were commonly identified as factors associated with NICU length of stay in all 5 models (P

Meta‐regression analyses usually focus on estimating and testing differences in average effect sizes between individual levels of each meta‐regression covariate in turn. These metrics are useful but have limitations: they consider each covariate individually, rather than in combination, and they characterize only the mean of a potentially heterogeneous distribution of effects. We propose additional metrics that address both limitations. Given a chosen threshold representing a meaningfully strong effect size, these metrics address the questions: "For a given joint level of the covariates, what percentage of the population effects are meaningfully strong?" and "For any two joint levels of the covariates, what is the difference between these percentages of meaningfully strong effects?" We provide semiparametric methods for estimation and inference and assess their performance in a simulation study. We apply the proposed methods to meta‐regression analyses on memory consolidation and on dietary behavior interventions, illustrating how the methods can provide more information than standard reporting alone. To facilitate implementing the methods in practice, we provide reporting guidelines and simple R code. [ABSTRACT FROM AUTHOR]

We propose instrumental variables (IV) estimators for averaged conditional treatment effects and the parameters upon which they depend in the context of a semiparametric outcome model with endogenous discrete treatment variables. For this model, the treatment impacts are unknown functions of a vector of indices that depend on a finite dimensional parameter vector. We develop the theory for an estimator of these impacts when they are averaged over regions of interest. We prove identification, consistency and N -asymptotic normality of the estimators. We also show that they are efficient under correct model specification. Further, we show that they are robust to misspecification of the propensity score model. In the Monte Carlo study, the estimators perform well over a wide variety of designs covering both correct and incorrect propensity score model specification. [ABSTRACT FROM AUTHOR]

A new and an enriched JPEG algorithm is provided for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function. Our main contribution is by formulating analytically (instead of approximating) the inverse of the transpose of JPEG-wavelet transform without involving matrices which are computationally cumbersome. The algorithm is suitable for the widely-spread situations where the original data distribution is unobservable such as in cases where there is deficient representation of the entire population in the training data (in machine learning) and thus the covariate shift assumption is violated. The proposed estimator corrects for both biases, the one generated by endogenous truncation and the one generated by endogenous covariates. Results from utilizing 2 000 000 different distribution functions verify the applicability and high accuracy of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed.

When addressing semiparametric problems with parametric restrictions (assumptions on the distribution), the efficient score (ES) of a parameter is often important for generating useful estimates. However, usual derivation of ES, although conceptually simple, is often lengthy and with many steps that do not help in understanding why its final form arises. This drawback often casts onto semiparametric estimation a mantle that can turn away otherwise able doctoral students or researchers. Here we show that many ESs can be obtained as a one-step derivation after we characterize those features (envelopes) of the unrestricted problem that are constrained in the restricted problem. We demonstrate our arguments in three problems with known ES but whose usual derivations are lengthy. We show that the envelope-based derivation is dramatically explanatory and compact, needing essentially two lines where the standard approach needs 10 or more pages. This suggests that the envelope method can add useful intuition and exegesis to both teaching and research of semiparametric estimation. [ABSTRACT FROM AUTHOR]

Profile monitoring is one of the methods used in statistical process control (SPC) to understand the functional relationship between response and explanatory variables by tracking this relationship and estimating parameters. SPC is done in two phases: In Phase I, a statistical model is created and its parameters estimated using historical data. Phase II implements the statistical model and monitors the live ongoing process. Control charts are graphical tools used to monitor these functional relationships over time in both Phase I and Phase II. This study provides a step‐by‐step application for parametric, nonparametric, and semiparametric methods in profile monitoring and creates an in‐depth guideline with comparative analysis studies for novice practitioners. A comparative analysis under each distributional assumption is conducted for various control charts. [ABSTRACT FROM AUTHOR]

This research is motivated by discovering and underpinning genetic causes for the progression of a bilateral eye disease, age-related macular degeneration (AMD), of which the primary outcomes, progression times to late-AMD, are bivariate and interval-censored due to intermittent assessment times. We propose a novel class of copula-based semiparametric transformation models for bivariate data under general interval censoring, which includes the case 1 interval censoring (current status data) and case 2 interval censoring. Specifically, the joint likelihood is modeled through a two-parameter Archimedean copula, which can flexibly characterize the dependence between the two margins in both tails. The marginal distributions are modeled through semiparametric transformation models using sieves, with the proportional hazards or odds model being a special case. We develop a computationally efficient sieve maximum likelihood estimation procedure for the unknown parameters, together with a generalized score test for the regression parameter(s). For the proposed sieve estimators of finite-dimensional parameters, we establish their asymptotic normality and efficiency. Extensive simulations are conducted to evaluate the performance of the proposed method in finite samples. Finally, we apply our method to a genome-wide analysis of AMD progression using the Age-Related Eye Disease Study data, to successfully identify novel risk variants associated with the disease progression. We also produce predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics. [ABSTRACT FROM AUTHOR]

We study explained variation under the additive hazards regression model for right-censored data. We consider different approaches for developing such a measure, and focus on one that estimates the proportion of variation in the failure time explained by the covariates. We study the properties of the measure both analytically, and through extensive simulations. We apply the measure to a well-known survival dataset as well as the linked surveillance, epidemiology, and end results-Medicare database for prediction of mortality in early stage prostate cancer patients using high-dimensional claims codes. [ABSTRACT FROM AUTHOR]