Nonparametric discrimination of areal functional data

Younso, Ahmad

doi:10.1214/18-bjps418

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…Consequently, the interest in and need for tools that tackle the analysis of functional datasets is increasing significantly. For example, consult Silverman (2002, 2006), Ferraty and Vieu (2006), Rao (2010), Horvath and Kokoszka (2012), Cuevas (2014), Hsing and Eubank (2015), Kokoszka and Reimherr (2017), Younse (2020), and Martines-Hernandez and Genton (2020) for recent developments on the theory and applications of functional data analysis tools.…”

Section: Introductionmentioning

confidence: 99%

A partial least squares approach for function-on-function interaction regression

Beyaztaş

Shang

2021

Comput Stat

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The function-on-function linear regression model in which the response and predictors consist of random curves has become a general framework to investigate the relationship between the functional response and functional predictors. Existing methods to estimate the model parameters may be sensitive to outlying observations, common in empirical applications. In addition, these methods may be severely affected by such observations, leading to undesirable estimation and prediction results. A robust estimation method, based on iteratively reweighted simple partial least squares, is introduced to improve the prediction accuracy of the function-on-function linear regression model in the presence of outliers. The performance of the proposed method is based on the number of partial least squares components used to estimate the function-on-function linear regression model. Thus, the optimum number of components is determined via a data-driven error criterion. The finite-sample performance of the proposed method is investigated via several Monte Carlo experiments and an empirical data analysis. In addition, a nonparametric bootstrap method is applied to construct pointwise prediction intervals for the response function. The results are compared with some of the existing methods to illustrate the improvement potentially gained by the proposed method.

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Section: Introductionmentioning

confidence: 99%

A partial least squares approach for function-on-function interaction regression

Beyaztaş

Shang

2021

Comput Stat

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“…This model is mostly used to classify data produced by social network analysis taking into account the connection between nodes, but without any influence of the spatial coordinates. [18,17,19,20,21] deal with kernel-based rules to classify temporally and spatially dependent data, and study asymptotic properties of classifiers. The aim of the present paper is to investigate whether the classical k-nearest neighbor classifier can be extended to classify spatial data.…”

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confidence: 99%

“…The main difficulties with the kernel method appear when data are sparse; choosing the number of neighbors allows to avoid this problem and is adapted to the concentration of the data. Consistency of kernel-based rules on temporally or spatially dependent data has recently been investigated by [18,17,19,20,21] in finite and infinite-dimensional space. In this paper, we will establish the (strong) consistency of the k-nearest neighbor classifier for spatially dependent data.…”

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Consistency of the k-Nearest Neighbor Classifier for Spatially Dependent Data

Younso

Kanaya²,

Azhari³

2022

Commun. Math. Stat.

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The purpose of this paper is to investigate the k-nearest neighbor classification rule for spatially dependent data. Some spatial mixing conditions are considered, and under such spatial structures, the well known k-nearest neighbor rule is suggested to classify spatial data. We established consistency and strong consistency of the classifier under mild assumptions. Our main results extend the consistency result in the i.i.d. case to the spatial case.Analysis of spatial data arises in various areas of research including agricultural field trials, astronomy, econometrics, epidemiology, environmental science, geology, hydrology, image analysis, meteorology, ecology, oceanography and many others in which the data of interest are collected across space. One of the most fundamental issues in spatial analysis is classification and pattern

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A robust partial least squares approach for function-on-function regression

Beyaztaş

Shang

2022

Braz. J. Probab. Stat.

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Nonparametric discrimination of areal functional data

Cited by 3 publications

References 31 publications

A partial least squares approach for function-on-function interaction regression

A partial least squares approach for function-on-function interaction regression

Consistency of the k-Nearest Neighbor Classifier for Spatially Dependent Data

A robust partial least squares approach for function-on-function regression

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