Estimation Strategy Research Articles

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

From the pandemic to the pitch. Unraveling COVID-19′s effect on workers' performance

There is a growing body of literature on the impact of COVID-19 on workers' performance upon recovery. This paper explores that question using granular data from professional athletes. Using a difference-in-difference estimation strategy and estimating an n-dimensional performance index, we find that performance drops upon recovery during the first thirty days after infection by 13,6%. Taking advantage of Colombia's mountainous geography, our results indicate that the main driver of performance drop in athletes is performance at high altitudes.

Optimal Strategy for Elevated Estimation of Population Mean in Stratified Random Sampling under Linear Cost Function

Optimal Strategy for Elevated Estimation of Population Mean in Stratified Random Sampling under Linear Cost Function

Innovative Approaches for Dynamic System Monitoring: Signal Processing and Parameter Estimation Strategies

Dynamic system monitoring is essential for ensuring the optimal performance and reliability of various systems across multiple domains. This Abstract introduces innovative approaches focusing on signal processing and parameter estimation strategies for dynamic system monitoring. Signal processing techniques such as wavelet transform and adaptive filtering are utilized for noise reduction and feature extraction from sensor data. Additionally, parameter estimation strategies including Kalman filtering and Bayesian inference aid in accurately estimating system parameters and states in real-time. These advanced methods, integrating machine learning and statistical inference, promise enhanced monitoring capabilities, facilitating proactive maintenance and fault detection in complex dynamic systems. Through case studies and simulation results, the effectiveness and versatility of these approaches in addressing real-world challenges are demonstrated, illustrating their potential for advancing the field of dynamic system monitoring.

Training flexible spatial-cognitive estimation strategies using augmented reality

We investigated the potential for augmented reality (AR) as a training aid for spatial estimation skills. Though there are many tools to support spatial judgments, from measuring cups to rulers, not much is known about training spatial skills for retention and transfer. Display of AR was manipulated to train the spatial skill of portion estimation. In Experiment 1, an AR-aided strategy of creating smaller portions out of a larger example amount was compared to a no-AR control condition. This manipulation was based on previous non-AR experiments where amorphous foods were better estimated when divided into smaller portions. There was a significant benefit of estimating using a solid AR shape. In Experiment 2, cognitive anchoring was manipulated. Using meaningful AR anchors resulted in the best performance and most learning. We conclude that spatial estimation skills can be combined with mental strategies and trained via AR.

Towards accurate correspondence between BIM and construction using high-dimensional point cloud feature tensor

The correspondence between BIM and construction instances is crucial to construction management. However, spatial deviation and geometric heterogeneity between BIM and construction point clouds pose great challenges. This paper establishes high-dimensional point cloud feature tensor to devise a point cloud semantic segmentation network and an incremental point-to-point correspondence estimation strategy against spatial deviation and geometric heterogeneity. In the construction of the stadium for the 31st Summer World University Games, the method achieved semantic segmentation accuracy (OA) of 93.8% - 99.9% and reduced BIM-and-construction correspondence error from 16 cm to 3 cm, and automatically documented four-phase construction progresses of 38 317 instances with progress monitoring error of 1%. These results demonstrate the effectiveness and applicability of the proposed method in BIM-to-construction semantic transfer and BIM-and-construction instance correspondence for large complex buildings. Future research will design transfer learning networks to achieve fully BIM-driven semantic understanding and knowledge mining for intelligent construction.

Poverty, work intensity, and disability: evidence from European countries.

We use 2015-2018 European Union Statistics on Income and Living Conditions longitudinal data for four European countries (the UK, Germany, France, and Italy) and a dynamic trivariate panel data model to analyze the complex relationship between poverty, work intensity, and disability. We find evidence of genuine state dependence in the three processes and feedback effects from past poverty to work intensity in all countries and from past poverty to disability in the UK, Germany, and Italy. Disability is detrimental to poverty, despite the mitigating role played by disability cash benefits. The magnitude of this effect seems to be associated with the average expenditure on social protection benefits and its distribution across functions. We stress the importance of accounting for the extra costs of disability and the key role work intensity plays in the disability-poverty connection. Finally, adopting a joint estimation strategy appears crucial to consistently estimating the relationship between the three processes.

Estimating the impact of container port throughput on employment: an analysis for African countries with seaports

Ports play a significant role in facilitating international trade and economic development, serving as vital gateways for the movement of goods across the continent and beyond. As global trade volumes continue to rise, efficient port operations hold the potential to not only enhance economic growth but also contribute significantly to job creation across various sectors of the economy. This paper examines the impact of container port throughput on employment in Africa and further tests whether causality runs from employment to container port throughput. To do so, we use a sample of 27 African countries with seaport and data spanning the period from 2010 to 2020 for the analysis. The system- Generalized Method of Moments (SGMM) estimation technique is used as the estimation strategy. We use service, industrial, and total employment percentages of the total population as proxies for employment while annual container throughput measured in Twenty foots Equivalent Units (TEUs) is used as an indicator for port throughput. Based on the empirical results, we establish a positive significant effect of port throughput on employment in Africa. We further show that bidirectional causality exists between port throughput and employment in Africa. Following these findings, we recommend policies that increase port throughput in Africa.

Robust estimation of a regression function in exponential families

We observe n pairs of independent (but not necessarily i.i.d.) random variables X1=(W1,Y1),…,Xn=(Wn,Yn) and tackle the problem of estimating the conditional distributions Qi⋆(wi) of Yi given Wi=wi for all i∈{1,…,n}. Even though these might not be true, we base our estimator on the assumptions that the data are i.i.d. and the conditional distributions of Yi given Wi=wi belong to a one parameter exponential family Q¯ with parameter space given by an interval I. More precisely, we pretend that these conditional distributions take the form Qθ(wi)∈Q¯ for some θ that belongs to a VC-class Θ¯ of functions with values in I. For each i∈{1,…,n}, we estimate Qi⋆(wi) by a distribution of the same form, i.e. Qθ̂(wi)∈Q¯, where θ̂=θ̂(X1,…,Xn) is a well-chosen estimator with values in Θ¯. We establish non-asymptotic exponential inequalities for the upper deviations of a Hellinger-type distance between the true conditional distributions of the data and the estimated one based on the exponential family Q¯ and the class of functions Θ¯ we chose. We show that our estimation strategy is robust to model misspecification, contamination and the presence of outliers. Besides, when the data are truly i.i.d., the exponential family Q¯ is suitably parametrized and the conditional distributions Qi⋆(wi) of the form Qθ⋆(wi)∈Q¯ for some unknown Hölderian function θ⋆ with values in I, we prove that the estimator θ̂ of θ⋆ is minimax (up to a logarithmic factor). Finally, we provide an algorithm for calculating θ̂ when Θ¯ is a VC-class of functions of low or moderate dimension and we carry out a simulation study to compare its performance to that of the MLE and median-based estimators. The proof of our main result relies on an upper bound, with explicit numerical constants, on the expectation of the supremum of an empirical process over a VC-subgraph class. This bound can be of independent interest.

Multi-Objective Bayesian Optimization with Active Preference Learning

There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, identifying the whole Pareto front requires the prohibitive search cost, while in many practical scenarios, the decision maker (DM) only needs a specific solution among the set of the Pareto optimal solutions. We propose a Bayesian optimization (BO) approach to identifying the most preferred solution in the MOO with expensive objective functions, in which a Bayesian preference model of the DM is adaptively estimated by an interactive manner based on the two types of supervisions called the pairwise preference and improvement request. To explore the most preferred solution, we define an acquisition function in which the uncertainty both in the objective function and the DM preference is incorporated. Further, to minimize the interaction cost with the DM, we also propose an active learning strategy for the preference estimation. We empirically demonstrate the effectiveness of our proposed method through the benchmark function optimization and the hyper-parameter optimization problems for machine learning models.

Sampling-Resilient Multi-Object Tracking

Multi-Object Tracking (MOT) is a cornerstone operator for video surveillance applications. To enable real-time processing of large-scale live video streams, we study an interesting scenario called down-sampled MOT, which performs object tracking only on a small subset of video frames. The problem is challenging for state-of-the-art MOT methods, which exhibit significant performance degradation under high frame reduction ratios. In this paper, we devise a sampling-resilient tracker with a novel sparse-observation Kalman filter (SOKF). It integrates an LSTM network to capture non-linear and dynamic motion patterns caused by sparse observations. Since the LSTM-based state transition is not compatible with the original noise estimation mechanism, we propose new estimation strategies based on Bayesian neural networks and derive the optimal Kalman gain for SOKF. To associate the detected bounding boxes robustly, we also propose a comprehensive similarity metric that systematically integrates multiple spatial matching signals. Experiments on three benchmark datasets show that our proposed tracker achieves the best trade-off between efficiency and accuracy. With the same tracking accuracy, we reduce the total processing time of ByteTrack by 2× in MOT17 and 3× in DanceTrack.

A semantic SLAM-based method for navigation and landing of UAVs in indoor environments

Autonomous navigation and landing of Unmanned aerial vehicles (UAVs) are critical functions towards full intelligence in unknown environments. However, current methods heavily rely on positioning devices or geometric maps, leading to two limitations: (1) UAVs fail to work normally indoors when signals are blocked, and (2) Navigation systems are not capable of achieving high-level decision-making missions. In this paper, we propose a novel and complete framework to realize the autonomous landing of UAVs in unknown indoor scenes based on visual SLAM, semantic segmentation, terrain estimation, and a decision-making model. Firstly, our 3D map is built on top of the visual SLAM system with semantic features, and then multiple types of maps (e.g., texture map, octo-map, grid-map, and semantic topology) are created for different function requirements. To achieve high-level scene understanding, we design a data association rule to fuse semantic features extracted by a deep learning model into the mapping process of topology structure. Next, a terrain estimation strategy is performed in the lightweight grid-map, which is modeled only via low-level elevation representation. Through multiple terrain constraints factor, a few optimized landing sites are selected in safe regions via clustering algorithm. Finally, based on the guidance of semantic topology, we construct a decision-making model for UAV landing that effectively integrates terrain safety, environment perception, and path planning. We perform extensive autonomous landing experiments on multiple indoor scenes from the TUM RGB-D dataset, and the full unknown environment maps are created simultaneously. The experiment proves that the proposed framework can select an optimal landing site for the advanced mission of UAVs effectively.

Abstract 845: Population attributable fraction of cancer incidence due to infectious agents in 2020 in the Republic of Korea

Abstract Purpose: Earlier in 2009, National Cancer Center Korea estimated population attributable fraction (PAF) of cancer incidence due to infectious agents as 24.5% in men and 15.4% in women. This study aimed to investigate the proportion of cancer incidence attributable to infectious agents in the Korean population as of 2020. Materials and Methods: We organized the Consensus Committee to establish strategy and methodology of PAF estimation. Based on the consensus, infectious agents and cancer sites for PAF estimation were selected according to the IARC List of classification with sufficient evidence in human. As a result, infectious agents included hepatitis B and C virus (HBV and HCV), Helicobacter pylori (H. pylori), human papilloma virus (HPV), Clonorchis sinensis (C.sinensis), Epstein Barr virus, and human immunodeficiency virus, and cancer sites included hepatocellular carcinoma (HCC), cholangiocarcinoma (CCC), gastric cancer, pancreatic cancer, cervical cancer, and non-Hodgkin lymphoma. The PAF was calculated by using Levin’s formula including exposure prevalence and relative risks (RRs). Exposure prevalence data were collected from literature review of studies in Korean population in 2005 or earlier, assuming at least 15 years of latent period from risk exposure to cancer development. We performed systematic review for cohort studies and case-control studies which investigated RRs or ORs of cancer due to infectious agents in Korean population. Pooled RRs were estimated by meta-analysis of RRs or ORs by using random-effects model. Results: The fractions of all cancer incidence attributable to infectious agents were estimated to be 13.5% and 7.1% in men and women, respectively. The reduction in PAFs of cancer incidence due to infection in 2020 compared to 2009 is considered due to decrease in exposure prevalence. The major findings regarding infectious agents with large PAFs are as the following. We found that the fractions of HCC cases attributable to HBV infection were 47.7% and 38.4% for cancer incidence in men and women, respectively. The estimated PAFs of gastric cancer due to H. pylori were 40.4% in men and 38.2% in women. C. sinensis was related to 9.8% of CCC cases in men and 5.2% in women. A total of 77.7% of cervical cancer incidence was attributed to HPV infection. Conclusion: Although the total PAF due to infection has decreased over the last decade, infection still contributes a substantial fraction to cancer incidence in Korea. These recent estimates of cancer burden due to infection are important for planning and evaluating the national cancer prevention programs. Citation Format: Kyung-Shin Lee, Hwa Jin Lee, Yoon-Jung Choi, Moran Ki, Aesun Shin, Jong-Hun Kim, Sue-Kyung Park, So-Seul Sung, Sung Ji Moon, Hong Gwan Seo, Jin-Kyoung Oh, Jeong-Soo Im. Population attributable fraction of cancer incidence due to infectious agents in 2020 in the Republic of Korea [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 845.

Abstract 2216: Population attributable fraction of tobacco smoking on cancer incidence in 2020 in the Republic of Korea

Abstract Backgrounds: Cancer is the most common cause of mortality in Korea. According to International Agency for Research on Cancer (IARC), tobacco smoking is the Group I carcinogen, and the National Cancer Center Korea reported in 2009 that 20.9% and 2.3% of total cancer incidence were attributable to tobacco smoking in men and women, respectively. As of 2020, we estimated population attributable fraction (PAF) of cancer incidence due to tobacco smoking. Methods: We organized the Consensus Committee to develop strategy and methodology for PAF estimation. From the consensus, cancer sites for PAF estimation were selected based on the IARC list of classification with sufficient evidence in human. PAF was calculated by using Levin’s formula which includes exposure prevalence and relative risk (RR). Exposure prevalence was collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2005, assuming that latent period from risk exposure to cancer development was 15 years. Tobacco smoking was defined as a categorical variable including non-smokers, past smokers, and current smokers. Pooled RRs were estimated from meta-analysis of RRs obtained from Korean Genome and Epidemiology Study, National Health Examination data provided by National Health Insurance Services, Korean Cancer Prevention Study-II, and KNHANES, each of which was linked with Korea Cancer Registry. Cox proportional hazards model were used to estimate RRs, while adjusted by age groups and alcohol consumption. Results: For men, 17.4% of all cancer cases were attributed to smoking, compared to 1.7% for women. Specifically, in men, 57.9% of lung cancer, 36.4% of bladder cancer, 31.9% of esophageal cancer, 39.1% of laryngeal cancer, 25.4% of oral cavity/pharyngeal cancer, 19.7% of liver cancer, and 19.5% of stomach cancer were attributed to tobacco smoking. In women, 51.7% of laryngeal cancer, 15.0% of esophageal cancer, 11.6% of lung cancer, 6.0% of bladder cancer, 5.1% of oral cavity/pharyngeal cancer, 5.0% of cervix cancer, and 4.6% of liver cancer were due to smoking. Conclusion: Tobacco smoking is an important cause of esophageal, laryngeal, and lung cancer in men, and esophageal and laryngeal cancer in women, and comprises a substantial proportion of PAF in cancer incidence. Thus, intervention measures are needed to reduce smoking prevalence for cancer prevention in Korea. Citation Format: Su Jung Byoun, Yoon-Jung Choi, Ye Lin Min, Sue-Kyung Park, Kwang Pil Ko, So_Seul Sung, NaYoung Kim, Keum Ji Jung, Sun Ha Jee, Sung-II Cho, Min Kyung Lim, Sohee Park, Eun Young Park, Hae Dong Woo, Kyu-Won Jung, E Hwa Yun, Byungmi Kim, Jin-Kyoung Oh, Yoonjoo Choi, Bohyun Park, Hong Gwan Seo, Jeong-Soo Im. Population attributable fraction of tobacco smoking on cancer incidence in 2020 in the Republic of Korea [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2216.

Abstract 2201: Population attributable fraction of alcohol consumption on cancer incidence in 2020 in the Republic of Korea

Abstract Backgrounds: Alcohol consumption is the Group I carcinogen according to International Agency for Research on Cancer (IARC). In 2009, National Cancer Center Korea reported that 3.0% and 0.5% of total cancer incidence were attributable to alcohol consumption in men and women, respectively. Herein, we estimated population attributable fraction (PAF) of cancer incidence due to alcohol consumption as of 2020. Methods: We organized the Consensus Committee to establish strategy and methodology of PAF estimation. Based on the consensus, cancer sites for PAF estimation were selected according to the IARC List of classification with sufficient evidence in human. PAF was calculated by using Levin’s formula which incorporates exposure prevalence and relative risk (RR). Exposure prevalence was obtained from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2005, assuming that latent period between exposure to alcohol consumption and diagnosis of cancer would be 15 years. Alcohol consumption was defined as non-drinking (<0.1g/day), light (0.1~30g/day), moderate (30~60g/day), and heavy (60~588g/day) drinking according to the definition by World Health Organization. We estimated RRs from the cohort data established by Korean Genome and Epidemiology Study, National Health Examination data provided by National Health Insurance Services, Korean Cancer Prevention Study-II, and KNHANES, while each cohort was linked with Korea Cancer Registry. RRs were estimated by using Cox proportional hazards model adjusted by age group and smoking status. RRs were then meta-analyzed to yield pooled RR by using random-effects model. Results: A total of 4.1% and 1.2% of all cancer incidence were attributable to alcohol consumption in men and women. By cancer sites, 67.1% of esophageal cancer, 23.8% of oral cavity/pharyngeal cancer, 15.1% of laryngeal cancer, 12.0% of colorectal cancer, and 7.9% of liver cancer were attributable to alcohol consumption in men. In women, 54.1% of liver cancer, 10.3% of colorectal cancer, 7.8% of oral cavity/pharyngeal cancer, and 3.6% of breast cancer were due to alcohol consumption. Conclusion: PAF of cancer incidence due to alcohol consumption has increased in 2020 compared to PAF in 2009. As alcohol consumption is an important cause of oral cavity/pharyngeal, esophageal, and laryngeal cancer in men, and liver and breast cancer in women, prevention measures are required to reduce alcohol consumption for cancer prevention in Korea. Citation Format: Ye Lin Min, YounJu Park, Su Jung Byoun, Sue_Kyung Park, Kwang Pil Ko, So-Seul Sung, Na Young Kim, Keum Ji Jung, Sun Ha Jee, Sung-Il Cho, Min Kyung Lim, Sohee Park, Eun Young Park, Hae Dong Woo, Kyu-Won Jung, E Hwa Yun, Byungmi Kim, Jin-Kyoung Oh, Yoonjoo Choi, Bohyun Park, Hong Gwan Seo, Yoon-Jung Choi, Jeong-Soo Im. Population attributable fraction of alcohol consumption on cancer incidence in 2020 in the Republic of Korea [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2201.

Stochastic emulation with enhanced partial‐ and no‐replication strategies for seismic response distribution estimation

AbstractModern performance earthquake engineering practices frequently require a large number of time‐consuming non‐linear time‐history simulations to appropriately address excitation and structural uncertainties when estimating engineering demand parameter (EDP) distributions. Surrogate modeling techniques have emerged as an attractive tool for alleviating such high computational burden in similar engineering problems. A key challenge for the application of surrogate models in earthquake engineering context relates to the aleatoric variability associated with the seismic hazard. This variability is typically expressed as high‐dimensional or non‐parametric uncertainty, and so cannot be easily incorporated within standard surrogate modeling frameworks. Rather, a surrogate modeling approach that can directly approximate the full distribution of the response output is warranted for this application. This approach needs to additionally address the fact that the response variability may change as input parameter changes, yielding a heteroscedastic behavior. Stochastic emulation techniques have emerged as a viable solution to accurately capture aleatoric uncertainties in similar contexts, and recent work by the second author has established a framework to accommodate this for earthquake engineering applications, using Gaussian Process (GP) regression to predict the EDP response distribution. The established formulation requires for a portion of the training samples the replication of simulations for different descriptions of the aleatoric uncertainty. In particular, the replicated samples are used to build a secondary GP model to predict the heteroscedastic characteristics, and these predictions are then used to formulate the primary GP that produces the full EDP distribution. This practice, however, has two downsides: it always requires minimum replications when training the secondary GP, and the information from the non‐replicated samples is utilized only for the primary GP. This research adopts an alternative stochastic GP formulation that can address both limitations. To this end, the secondary GP is trained by measuring the square of sample deviations from the mean instead of the crude sample variances. To establish the primitive mean estimates, another auxiliary GP is introduced. This way, information from all replicated and non‐replicated samples is fully leveraged for estimating both the EDP distribution and the underlying heteroscedastic behavior, while formulation accommodates an implementation using no replications. The case study examples using three different stochastic ground motion models demonstrate that the proposed approach can address both aforementioned challenges.

Educational choice, initial wage and wage growth

We study the effects of expected initial wages, expected wage growth, and observed and unobserved heterogeneity in the choice of college major in a sample of American college graduates. We propose a three-stage empirical model that relates future earnings to individual choices. In the first stage, starting from revealed choices, observed wages, and life-cycle wage profiles, we estimate the expectation on initial wages and wage growth from the individual point of view, where the panel structure of the data allows us to produce estimates corrected for self-selection bias. We find substantial differences in expected real wages and expected real wage growth between majors and that both characteristics of life cycle earnings influence major choice. Our parametric models show a strong correlation between salary trends and major choice, whereas semiparametric models yield less reliable results. We interpret our results as being consistent with agents being rational and as a validation for our estimation strategy based on counterfactual imputation.

A computational strategy for estimation of mean using optimal imputation in presence of missing observation.

In this study, we suggest an optimal imputation strategy for the elevated estimation of the population mean of the primary variable utilizing the known auxiliary parameters for the missing observations. Under this strategy, we suggest a new modified Searls type estimator, and we study its sampling properties, mainly bias and mean squared error (MSE), for an approximation of order one. The introduced estimator is compared theoretically with the estimators of population mean in competition under the imputation method. The efficiency conditions for the introduced estimator to be more efficient than the estimators in the competition are derived. To be sure about the efficiencies, these efficiency conditions are verified through the three natural populations. We have also conducted a simulation study and generated an artificial population with the same parameters as a natural population. The estimator with minimum MSE and the highest Percentage Relative Efficiency (PRE) is recommended for practical use in different areas of applications.

Functional mixtures-of-experts

AbstractWe consider the statistical analysis of heterogeneous data for prediction, in situations where the observations include functions, typically time series. We extend the modeling with mixtures-of-experts (ME), as a framework of choice in modeling heterogeneity in data for prediction with vectorial observations, to this functional data analysis context. We first present a new family of ME models, named functional ME (FME), in which the predictors are potentially noisy observations, from entire functions. Furthermore, the data generating process of the predictor and the real response, is governed by a hidden discrete variable representing an unknown partition. Second, by imposing sparsity on derivatives of the underlying functional parameters via Lasso-like regularizations, we provide sparse and interpretable functional representations of the FME models called iFME. We develop dedicated expectation–maximization algorithms for Lasso-like regularized maximum-likelihood parameter estimation strategies to fit the models. The proposed models and algorithms are studied in simulated scenarios and in applications to two real data sets, and the obtained results demonstrate their performance in accurately capturing complex nonlinear relationships and in clustering the heterogeneous regression data.

Analyzing health outcomes measured as bounded counts

This paper assesses analytical strategies that respect the bounded-count nature of health outcomes encountered often in empirical applications. Absent in the literature is a comprehensive discussion and critique of strategies for analyzing and understanding such data. The paper's goal is to provide an in-depth consideration of prominent issues arising in and strategies for undertaking such analyses, emphasizing the merits and limitations of various analytical tools empirical researchers may contemplate. Three main topics are covered. First, bounded-count health outcomes' measurement properties are reviewed and their implications assessed. Second, issues arising when bounded-count outcomes are the objects of concern in evaluations are described. Third, the (conditional) probability and moment structures of bounded-count outcomes are derived and corresponding specification and estimation strategies presented with particular attention to partial effects. Many questions may be asked of such data in health research and a researcher's choice of analytical method is often consequential.