Nonparametric estimation of circular trend surfaces with application to wave directions

Meilán-Vila, Andrea; Crujeiras, Rosa M.; Francisco‐Fernández, Mario

doi:10.1007/s00477-020-01919-5

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…Note also that our real data application considers a single location, but if data collection presents also a high spatial frequency, it would be possible to proceed with a spatial analysis. For this, an extension to the infinite-dimensional context of the regression model assumed in Meilán-Vila et al (2021a) could be considered, using the results obtained in Ruiz-Medina and Espejo (2012) or in Aguilera-Morillo et al (2017) for spatial functional data as part of the analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Following similar ideas, Meilán-Vila et al (2021b) proposed and studied local polynomial-type regression estimators considering a model with a circular response and several real-valued covariates for independent data. This class of estimators was also analyzed in Meilán-Vila et al (2021a) in the presence of spatial correlation.…”

Section: Introductionmentioning

confidence: 99%

“…This way of proceeding has already been considered previously. For instance, a similar approach was employed in Meilán-Vila et al (2021b) and Meilán-Vila et al (2021a) for regression models with a circular response, Euclidean covariates and independent or spatially correlated errors, respectively. As in any nonparametric approach, a crucial issue is the selection of an appropriate bandwidth or smoothing parameter.…”

Section: Introductionmentioning

confidence: 99%

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A flexible functional-circular regression model for analyzing temperature curves

Meilán-Vila¹,

Crujeiras²,

Francisco‐Fernández³

2022

Preprint

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Changes on temperature patterns, on a local scale, are perceived by individuals as the most direct indicators of global warming and climate change. As a specific example, for an Atlantic climate location, spring and fall seasons should present a mild transition between winter and summer, and summer and winter, respectively. By observing daily temperature curves along time, being each curve attached to a certain calendar day, a regression model for these variables (temperature curve as covariate and calendar day as response) would be useful for modeling their relation for a certain period. In addition, temperature changes could be assessed by prediction and observation comparisons in the long run. Such a model is presented and studied in this work, considering a nonparametric Nadaraya-Watson-type estimator for functional covariate and circular response. The asymptotic bias and variance of this estimator, as well as its asymptotic distribution are derived. Its finite sample performance is evaluated in a simulation study and the proposal is applied to investigate a real-data set concerning temperature curves.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A flexible functional-circular regression model for analyzing temperature curves

Meilán-Vila¹,

Crujeiras²,

Francisco‐Fernández³

2022

Preprint

Self Cite

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“…Abuzaid (2020) extends the local outlier factor (LOF) application to the detection of potential outliers in circular samples, where the angles of the circular data are represented in two Cartesian coordinates and treated as bivariate data. Recently, Meilán-Vila, Crujeiras and Francisco-Fernández (2021) considers a spatial regression model with a circular response and several real-valued predictors and proposes nonparametric estimators of the circular trend surface that account for spatial correlation. Jha, Biswas and Cheng (2022) propose a paper that uses the Möbius transformation-based link function to address robustness issues in circular-circular regression.…”

Section: Introductionmentioning

confidence: 99%

Detection of Outliers in Circular Regression Model Via DFBETAcIS Statistic

Ramlee,

Ibrahim,

Leow

et al. 2024

JSM

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The outlier issues in circular regression models have recently received much attention. The presence of outliers may cause the sign and magnitude of regression coefficients to vary, resulting in inaccurate model development and incorrect prediction. Many methods for detecting outliers in a circular regression model have been proposed in previous studies such as COVRATIO, D, M, A, and Chord statistics, but it is suspected that they are not very successful in the presence of multiple outliers in a data set since the masking and swamping is not considered in their studies. This study aimed to develop an outlier detection procedure using DFBETAc statistic for circular cases, where this new statistic will investigate and identify multiple outliers in the Jammalamadaka and Sarma circular regression model (JSCRM) by considering masking and swamping effect. Monte Carlo simulations are used to determine the corresponding cut-off point and the power of performance is investigated. The performance of the proposed statistic is evaluated by the proportion of detected outliers and the rate of masking and swamping. The simulation procedure is applied at 10% and 20% contamination levels for varying sample sizes. The results show that the proposed DFBETAcIS statistic for JSCRM successfully detect the outliers. For illustration purposes, this process is applied to wind direction data.

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“…Meilán-Vila et al [ 12 ] introduced local linear estimators for non-parametric multiple regression when the response variable was circular. Meilán-Vila et al [ 13 ] proposed circular trend surface estimators considering a spatial linear-circular non-parametric regression model. Recently, Di Marzio et al [ 14 ] has addressed the problem of estimating the Kernel regression function in the presence of measurement errors when the predictor and/or response variable is circular.…”

Section: Introductionmentioning

confidence: 99%

A method for detecting outliers in linear-circular non-parametric regression

Sert,

Kardiyen

2023

PLoS ONE

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This study proposes a robust outlier detection method based on the circular median for non-parametric linear-circular regression in case the response variable includes outlier(s) and the residuals are Wrapped-Cauchy distributed. Nadaraya-Watson and local linear regression methods were employed to obtain non-parametric regression fits. The proposed method’s performance was investigated by using a real dataset and a comprehensive simulation study with different sample sizes, contamination, and heterogeneity degrees. The method performs quite well in medium and higher contamination degrees, and its performance increases as the sample size and the homogeneity of data increase. In addition, when the response variable of linear-circular regression contains outliers, the Local Linear Estimation method fits the data set better than the Nadaraya Watson method.

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Nonparametric estimation of circular trend surfaces with application to wave directions

Cited by 5 publications

References 31 publications

A flexible functional-circular regression model for analyzing temperature curves

A flexible functional-circular regression model for analyzing temperature curves

Detection of Outliers in Circular Regression Model Via DFBETAcIS Statistic

A method for detecting outliers in linear-circular non-parametric regression

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