Nonparametric regression method with functional covariates and multivariate response

Omar, Kurdistan M.; Wang, Bo

doi:10.1080/03610926.2017.1410716

Cited by 4 publications

(2 citation statements)

References 15 publications

(19 reference statements)

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“…The local weighting models in the finite-dimensional data are extremely popular in the society of non-parameterizations, particularly the kernel, particularly the kernel. Ferraty and Vieu (2006) [10], Omar and Wang (2019) [11], and Midi et al (2021) [12] mention some fundamental statistical concepts on local weighting techniques and expansion to the functional data analysis as well kernel weighting in special regard. There are different types of kernel methods with a lot of automatic choices of bandwidth (smoothing parameter).…”

Section: Ramsay and Silverman (2005) And Cao Et Almentioning

confidence: 99%

Comparative Analysis of Predictive Performance in Nonparametric Functional Regression: A Case Study of Spectrometric Fat Content Prediction

Taher Omar,

Othman

2023

ijsmr

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Objective: This research aims to compare two nonparametric functional regression models, the Kernel Model and the K-Nearest Neighbor (KNN) Model, with a focus on predicting scalar responses from functional covariates. Two semi-metrics, one based on second derivatives and the other on Functional Principle Component Analysis, are employed for prediction. The study assesses the accuracy of these models by computing Mean Square Errors (MSE) and provides practical applications for illustration. Method: The study delves into the realm of nonparametric functional regression, where the response variable (Y) is scalar, and the covariate variable (x) is a function. The Kernel Model, known as funopare.kernel.cv, and the KNN Model, termed funopare.knn.gcv, are used for prediction. The Kernel Model employs automatic bandwidth selection via Cross-Validation, while the KNN Model employs a global smoothing parameter. The performance of both models is evaluated using MSE, considering two different semi-metrics. Results: The results indicate that the KNN Model outperforms the Kernel Model in terms of prediction accuracy, as supported by the computed MSE. The choice of semi-metric, whether based on second derivatives or Functional Principle Component Analysis, impacts the model's performance. Two real-world applications, Spectrometric Data for predicting fat content and Canadian Weather Station data for predicting precipitation, demonstrate the practicality and utility of the models. Conclusion: This research provides valuable insights into nonparametric functional regression methods for predicting scalar responses from functional covariates. The KNN Model, when compared to the Kernel Model, offers superior predictive performance. The selection of an appropriate semi-metric is essential for model accuracy. Future research may explore the extension of these models to cases involving multivariate responses and consider interactions between response components.

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Section: Ramsay and Silverman (2005) And Cao Et Almentioning

confidence: 99%

Comparative Analysis of Predictive Performance in Nonparametric Functional Regression: A Case Study of Spectrometric Fat Content Prediction

Taher Omar,

Othman

2023

ijsmr

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“…Omar and Wang 18 expanded the independent response method to multivariate responses method with functional covariate in nonparametric functional regression which is applied with real data and simulated data then the new model results (multivariate responses model) are preferable than the independent response method. The paper proposed a new model to deal with multivariate responses variables and functional covariate.…”

Section: Introductionmentioning

confidence: 99%

K-Nearest Neighbor Method with Principal Component Analysis for Functional Nonparametric Regression

2022

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This paper proposed a new method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA)) for measureing the closeness between curves. Root Mean Square Errors is used for the implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when the covariates are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables model, results are more preferable than the independent response method. The models are demonstrated by both a simulation data and real data.

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A Nonlinear Transformation Methods Using Covid-19 Data in the Kurdistan Region

Ahmad

Ismaeel

2022

2022 International Conference on Computer Science and Software Engineering (CSASE)

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Nonparametric regression method with functional covariates and multivariate response

Cited by 4 publications

References 15 publications

Comparative Analysis of Predictive Performance in Nonparametric Functional Regression: A Case Study of Spectrometric Fat Content Prediction

Comparative Analysis of Predictive Performance in Nonparametric Functional Regression: A Case Study of Spectrometric Fat Content Prediction

K-Nearest Neighbor Method with Principal Component Analysis for Functional Nonparametric Regression

A Nonlinear Transformation Methods Using Covid-19 Data in the Kurdistan Region

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