Bias Reduction for Nonparametric and Semiparametric Regression Models

Cheng, Ming‐Yen; Huang, Tao; Liu, Peng; Peng, Heng

doi:10.5705/ss.202017.0058

Cited by 9 publications

(6 citation statements)

References 22 publications

(29 reference statements)

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“…Furthermore, models (3.1) and (3.2) are of DC expressions of regression. Such a structure is different from the composition methods in Lin et al (2018), Cheng et al (2018), Lin and Li (2008), Wang and Lin (2015), and Tong and Wang (2005). This is because these methods do not have DC structure and use a model-independent parameter (e.g., quantile and bandwidth) as an artificial covariate, which does not exist in the original model, but is identified from the estimation procedure.…”

Section: Modelingmentioning

confidence: 92%

A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint

Li¹,

Li²

2019

Preprint

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This paper establishes a global bias-correction divide-and-conquer (GBC-DC) rule for biased estimation under the case of memory constraint. In order to introduce the new estimation, a closed representation of the local estimators obtained by the data in each batch is adopted, aiming to formulate a pro forma linear regression between the local estimators and the true parameter of interest. Least square method is then used within this framework to composite a global estimator of the parameter. Thus, the main advantage over the classical DC method is that the new GBC-DC method can absorb the information hidden in the statistical structure and the variables in each batch of data. Consequently, the resulting global estimator is strictly unbiased even if the local estimator has a non-negligible bias. Moreover, the global estimator is consistent, and even can achieve root-n consistency, without the constraint on the number of batches. Another attractive feature of the new method is computationally simple and efficient, without use of any iterative algorithm and local bias-correction. Specifically, the proposed GBC-DC method applies to various biased estimations such as shrinkage-type estimation and nonparametric regression estimation. Detailed simulation studies demonstrate that the proposed GBC-DC approach is significantly bias-corrected, and the behavior is comparable with the full data estimation and is much better than the competitors.

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Section: Modelingmentioning

confidence: 92%

A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint

Li¹,

Li²

2019

Preprint

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“…In the process of estimating the regression function of the multiresponse multipredictor nonparametric regression (MMNR) model by using the developed penalized weighted least square (PWLS) optimization, we assume that g rk (t rki ) is a fixed function. Hence, we can estimate the smoothing spline component in the multiresponse multipredictor nonparametric regression (MMNR) model presented in (3) for every response r = 1, 2, . .…”

Section: Penalized Weighted Least Square (Pwls) Optimizationmentioning

confidence: 99%

“…Until now, several estimators have been discussed by several previous researchers both theoretically and in application in several cases. Some of these estimators are local linear estimators, which are used to determine the boundary correction of regression function of the nonparametric regression model [2], to determine the bias reduction in the estimating regression function [3], and to design a standard growth chart used for assessing toddlers' nutritional status [4]; as a local polynomial estimator used to estimate regression functions in cases of errors-in-variable [5], in case of correlated errors [6], and to estimate the regression function of a functional data regression model [7] and finite population regression model [8]; as a kernel estimator used for estimating nonparametric regression function [9], for estimating and investigating the consistency property of a regression function [10], and for estimating regression function in case of correlated errors [11]. However, these estimators (i.e., local linear, local polynomial, and kernel) are less flexible because they are highly dependent on the neighborhood of the target point, called bandwidth, so that we need a small bandwidth to estimate a fluctuating data model, and this will cause a curve of estimation result that is too rough.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Multiresponse Multipredictor Nonparametric Regression Model Using Mixed Estimator

Chamidah,

Lestari,

Budiantara

et al. 2024

Symmetry

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In data analysis using a nonparametric regression approach, we are often faced with the problem of analyzing a set of data that has mixed patterns, namely, some of the data have a certain pattern and the rest of the data have a different pattern. To handle this kind of datum, we propose the use of a mixed estimator. In this study, we theoretically discuss a developed estimation method for a nonparametric regression model with two or more response variables and predictor variables, and there is a correlation between the response variables using a mixed estimator. The model is called the multiresponse multipredictor nonparametric regression (MMNR) model. The mixed estimator used for estimating the MMNR model is a mixed estimator of smoothing spline and Fourier series that is suitable for analyzing data with patterns that partly change at certain subintervals, and some others that follow a recurring pattern in a certain trend. Since in the MMNR model there is a correlation between responses, a symmetric weight matrix is involved in the estimation process of the MMNR model. To estimate the MMNR model, we apply the reproducing kernel Hilbert space (RKHS) method to penalized weighted least square (PWLS) optimization for estimating the regression function of the MMNR model, which consists of a smoothing spline component and a Fourier series component. A simulation study to show the performance of proposed method is also given. The obtained results are estimations of the smoothing spline component, Fourier series component, MMNR model, weight matrix, and consistency of estimated regression function. In conclusion, the estimation of the MMNR model using the mixed estimator is a combination of smoothing spline component and Fourier series component estimators. It depends on smoothing and oscillation parameters, and it has linear in observation and consistent properties.

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“…Nonparametric and semi-parametric statistical biases modeling: E.g. to identify nonlinear relationships between predictions and responses to reduce model biases [32].…”

Section: Data Quality Mechanisms From the Taxonomy After The Collecti...mentioning

confidence: 99%

Taxonomy of Data Quality Metrics in Digital Citizen Science

Vaddepalli¹,

Palacin²,

Porras³

et al. 2023

Intelligent Sustainable Systems

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Data quality is key in the success of a citizen science project. Valid datasets serve as evidence for scientific research. Numerous projects have highlighted the ways in which participatory data collection can cause data quality issues due to human day-to-day practices and biases. Also, these projects have used and reported a myriad of techniques to improve data quality in different contexts. Yet, there is a lack of systematic analyses of these experiences to guide the design and of digital citizen science projects. We mapped 35 data quality issues of 16 digital citizen science projects and proposed a taxonomy with 64 mechanisms to address data quality issues before, during and after the data collection in digital citizen science projects. This taxonomy is built upon the analysis of literature reports (N=144), two urban experiments (participants=280), and expert interviews (N=11). Thus, we contribute to advance the development of systematic methods to improve the data quality in digital citizen science projects.

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Bias Reduction for Nonparametric and Semiparametric Regression Models

Cited by 9 publications

References 22 publications

A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint

A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint

Estimation of Multiresponse Multipredictor Nonparametric Regression Model Using Mixed Estimator

Taxonomy of Data Quality Metrics in Digital Citizen Science

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