Detection of outliers in simple circular regression models using the mean circular error statistic

Abuzaid, Ali; Hussin, Abdul Ghapor; Mohamed, Ibrahim

doi:10.1080/00949655.2011.602679

Cited by 25 publications

(10 citation statements)

References 12 publications

(18 reference statements)

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“…In 2013 the first version of the package isocirc was presented (Barragán et al, 2013), making available functions to perform constrained inference using isotonic regression (Rueda, Fernández, and Peddada, 2009;Fernàndez, Rueda, and Peddada, 2011). The CircOutlier (Ghazanfarihesari and Mashhad, 2016) collects functions to detect outliers in circular-circular regression as proposed in Abuzaid, Hussin, and Mohamed (2013). More recent is the Directional package (Tsagris et al, 2018), mostly linked to the textbook by Mardia and Jupp (1999).…”

Section: Existing Software For Circular and Directional Datamentioning

confidence: 99%

CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data

Lasinio

Santoro²,

Mastrantonio

2020

Journal of Statistical Computation and Simulation

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CircSpaceTime is the only R package currently available that implements Bayesian models for spatial and spatio-temporal interpolation of circular data. Such data are often found in applications where, among the many, wind directions, animal movement directions, and wave directions are involved. To analyze such data we need models for observations at locations s and times t, as the so-called geostatistical models, providing structured dependence assumed to decay in distance and time. The approach we take begins with Gaussian processes defined for linear variables over space and time. Then, we use either wrapping or projection to obtain processes for circular data. The models are cast as hierarchical, with fitting and inference within a Bayesian framework. Altogether, this package implements work developed by a series of papers; the most relevant being Jona Lasinio, Gelfand, and Jona Lasinio (2012); Wang and Gelfand (2014); Mastrantonio, Jona Lasinio, and Gelfand (2016). All procedures are written using Rcpp. Estimates are obtained by MCMC allowing parallelized multiple chains run. The implementation of the proposed models is considerably improved on the simple routines adopted in the research papers. As original running examples, for the spatial and spatio-temporal settings, we use wind directions datasets over central Italy.

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Section: Existing Software For Circular and Directional Datamentioning

confidence: 99%

CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data

Lasinio

Santoro²,

Mastrantonio

2020

Journal of Statistical Computation and Simulation

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“…Abuzaid et al [ 12 ] and Ibrahim et al [ 13 ] explored the problem on two types of circular regression models by observing the effect of removing one observation on the covariance matrix. Further, Abuzaid et al [ 14 ] proposed a residual measure using a cosine function to detect outliers in a linear circular regression model, where the relationship between the dependent and independent variables is strictly linear (see [ 10 ]). In this paper, we propose a new summary measure for the purpose of detecting outliers in terms of a simple measure of circular distance in DM circular regression model.…”

Section: Introductionmentioning

confidence: 99%

Procedure for Detecting Outliers in a Circular Regression Model

et al. 2016

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A number of circular regression models have been proposed in the literature. In recent years, there is a strong interest shown on the subject of outlier detection in circular regression. An outlier detection procedure can be developed by defining a new statistic in terms of the circular residuals. In this paper, we propose a new measure which transforms the circular residuals into linear measures using a trigonometric function. We then employ the row deletion approach to identify observations that affect the measure the most, a candidate of outlier. The corresponding cut-off points and the performance of the detection procedure when applied on Down and Mardia’s model are studied via simulations. For illustration, we apply the procedure on circadian data.

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“…On the other hand, only few studies of outliers in circular regression can be found in the literature. Abuzaid et al (2013Abuzaid et al ( , 2011 and Ibrahim et al (2013) explored the problem in two types of circular regression model using row deletion approach while Hussin et al (2010) discussed the detection of influential observation in a linear functional relationship model for circular data.…”

Section: Introductionmentioning

confidence: 99%