Circular data

Lee, Alan

doi:10.1002/wics.98

Cited by 80 publications

(52 citation statements)

References 89 publications

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“…Fisher [6, pp. 30-35], [5, pp.9-15], [1] and [4, pp.13-19] also introduce some processes of finding descriptive statistics. In the case of observations of directions in two dimensions, these may be represented as angles measured with respect to the starting point and a sense of rotation.…”

Section: Descriptive Statistics For Circular Datamentioning

confidence: 99%

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Logistic regression for circular data

Al‐Daffaie

Khan

2017

AIP Conference Proceedings

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Abstract.This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.

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Section: Descriptive Statistics For Circular Datamentioning

confidence: 99%

“…The most common type is linear data such as observations on income, age, weight, numbers of any item and so on. Whereas, according to [1] the second type occurs when directions are measured. He states that time data, for example, measured on a 24 hour clock may be considered as circular data by converting them to angular data.…”

Section: Introductionmentioning

confidence: 99%

Logistic regression for circular data

Al‐Daffaie

Khan

2017

AIP Conference Proceedings

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“…Bandwidth for the kernel density is evaluated using LCV method. (a) Kernel circular density estimates for Lewiston, Maine for 1951-1980(black) and 1981-2010 estimated using bandwidth optimized from complete data set ; n 5 60), (b) Kernel circular density estimates for Lewiston, Maine for 1951-1980(black) and 1981-2010 estimated using two different bandwidths evaluated from two 30 year blocks of data separately; in Figures 8a and 8b, probability density estimates are assessed for significance based on density estimates using resampled data. A median estimate is obtained from the ensemble of distributions resulting from bootstrap resampling (N 5 1000), (c) significant results estimated as described in Figure 8a for all stations, and (d) significant results estimated as described in Figure 8b for all stations.…”

Section: 1002/2014wr016399mentioning

confidence: 99%

“…Directional/circular statistics is a branch of statistics that deals with directions, where random variables are represented by angles measured with respect to some starting point and sense of rotation [Jammalamadaka and SenGupta, 2001]. Standard statistical methods used for analysis of ordinary linear data (for example, like computing the sample mean and the sample variance) are inappropriate for analysis of circular data [Mardia and Jupp, 2000;Jammalamadaka and SenGupta, 2001;Lee, 2010]. Judicious use of circular statistics provides an improved understanding of environmental variables modeled as circular random processes (for example, the timing of an event within a cycle).…”

Section: Introductionmentioning

confidence: 99%

Nonstationarity in seasonality of extreme precipitation: A nonparametric circular statistical approach and its application

Dhakal

Jain

Gray

et al. 2015

Water Resources Research

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Changes in seasonality of extreme storms have important implications for public safety, storm water infrastructure, and, in general, adaptation strategies in a changing climate. While past research on this topic offers some approaches to characterize seasonality, the methods are somewhat limited in their ability to discern the diversity of distributional types for extreme precipitation dates. Herein, we present a comprehensive approach for assessment of temporal changes in the calendar dates for extreme precipitation within a circular statistics framework which entails: (a) three measures to summarize circular random variables (traditional approach), (b) four nonparametric statistical tests, and (c) a new nonparametric circular density method to provide a robust assessment of the nature of probability distribution and changes. Two 30 year blocks (1951-1980 and 1981-2010) of annual maximum daily precipitation from 10 stations across the state of Maine were used for our analysis. Assessment of seasonality based on nonparametric approach indicated nonstationarity; some stations exhibited shifts in significant mode toward Spring season for the recent time period while some other stations exhibited multimodal seasonal pattern for both the time periods. Nonparametric circular density method, used in this study, allows for an adaptive estimation of seasonal density. Despite the limitation of being sensitive to the smoothing parameter, this method can accurately characterize one or more modes of seasonal peaks, as well as pave the way toward assessment of changes in seasonality over time.

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“…Lee, 2010) arise naturally in many scientific fields where observations are recorded as directions or angles. Such data are encountered in environmental science (Bulla et al, 2012;?…”

Section: Introductionmentioning

confidence: 99%

Invariance properties and statistical inference for circular data

Mastrantonio¹,

Lasinio²,

Maruotti³

et al. 2018

STAT SINICA

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Statistical inference on the circle may strongly depend on the chosen reference system. Here, we introduce necessary and sufficient conditions to avoid inferential problems and misinterpretation of parameter estimates for any circular distribution. The construction of invariant distributions, with respect to the reference system, is discussed by introducing specific properties. Numerical Statistica Sinica: Newly accepted Paper (accepted version subject to English editing) examples on artificial and real data are presented to corroborate and illustrate theoretical results.

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Circular data

Cited by 80 publications

References 89 publications

Logistic regression for circular data

Logistic regression for circular data

Nonstationarity in seasonality of extreme precipitation: A nonparametric circular statistical approach and its application

Invariance properties and statistical inference for circular data

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