• Title/Summary/Keyword: Circular statistics

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Circular Statistics in Musicology

  • Lee, Jeong-Ran;Oh, Hee-Seok
    • Communications for Statistical Applications and Methods
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    • v.15 no.2
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    • pp.273-282
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    • 2008
  • An essential aspect of music is structure. Beran (2004) introduced a method of comparing piano plays via circular statistics based on the fact that there is circular structure in music. We expand the application of this method to a pair of two pop songs and discuss the possibility of applying it to detecting musical plagiarism. Circular statistics provides an objective view point comparing the musical works.

Analysis of the Effect of Wind Direction on Ozone Level

  • Na, Jong-Hwa;Sung, Su-Jin;Yu, Hye-Kyung
    • Communications for Statistical Applications and Methods
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    • v.19 no.4
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    • pp.527-536
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    • 2012
  • In this paper we analyze the effect of circular variables such as wind direction, time and month on the ozone level. In particular, we examined the effect of wind direction by exploratory data analysis methods and provide the correlation and regression analyzes in the cases including all circular explanatory variables. In the analysis, we convert time and month variables to circular variables and analyze the effect of these variables on regression analysis; in addition, we also consider circular-circular regression. We used weather condition and air pollution data collected from Dongdaemoon district of Seoul in 2007.

Modeling Circular Data with Uniformly Dispersed Noise

  • Yu, Hye-Kyung;Jun, Kyoung-Ho;Na, Jong-Hwa
    • The Korean Journal of Applied Statistics
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    • v.25 no.4
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    • pp.651-659
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    • 2012
  • In this paper we developed a statistical model for circular data with noises. In this case, model fitting by single circular model has a lack-of-fit problem. To overcome this problem, we consider some mixture models that include circular uniform distribution and apply an EM algorithm to estimate the parameters. Both von Mises and Wrapped skew normal distributions are considered in this paper. Simulation studies are executed to assess the suggested EM algorithms. Finally, we applied the suggested method to fit 2008 EHFRS(Epidemic Hemorrhagic Fever with Renal Syndrome) data provided by the KCDC(Korea Centers for Disease Control and Prevention).

Hidden truncation circular normal distribution

  • Kim, Sung-Su;Sengupta, Ashis
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.797-805
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    • 2012
  • Many circular distributions are known to be not only asymmetric but also bimodal. Hidden truncation method of generating asymmetric distribution is applied to a bivariate circular distribution to generate an asymmetric circular distribution. While many other existing asymmetric circular distributions can only model an asymmetric data, this new circular model has great flexibility in terms of asymmetry and bi-modality. Some properties of the new model, such as the trigonometric moment generating function, and asymptotic inference about the truncation parameter are presented. Simulation and real data examples are provided at the end to demonstrate the utility of the novel distribution.

Exponential family of circular distributions

  • Kim, Sung-Su
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.6
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    • pp.1217-1222
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    • 2011
  • In this paper, we show that any circular density can be closely approximated by an exponential family of distributions. Therefore we propose an exponential family of distributions as a new family of circular distributions, which is absolutely suitable to model any shape of circular distributions. In this family of circular distributions, the trigonometric moments are found to be the uniformly minimum variance unbiased estimators (UMVUEs) of the parameters of distribution. Simulation result and goodness of fit test using an asymmetric real data set show usefulness of the novel circular distribution.

Circular regression using geodesic lines

  • Kim, Sung-su
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.5
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    • pp.961-966
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    • 2011
  • Circular variables are those that have a period in its range. Their examples include direction of animal migration, and time of drug administration, just to mention a few. Statistical analysis of circular variables is quite different from that of linear variable due to its periodic nature. In this paper, the author proposes new circular regression models using geodesic lines on the surface of the sample space of the response and the predictor variables.

A Study of Circular Sampling in Finite Population

  • Hae-Yong Lee
    • Communications for Statistical Applications and Methods
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    • v.3 no.3
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    • pp.161-168
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    • 1996
  • This paper describes a sampling method, which can be used instead of the simple random sampling without replacement(SRSWOR). This method, circular sampling, assumes that the sampling units of the population are arranged in circular format, and randomly selects as many as samples of contiguous units. Therefore this method gathers information quicker and easier than STSWOR. In certain circumstances, the reliability of this method is better than that of STSWOR. And of circular sampling would be applied to nonprobability could be determined. methods, the reliability of the sample results in terms of probability could be determined.

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Projected Circular and l-Axial Skew-Normal Distributions

  • Seo, Han-Son;Shin, Jong-Kyun;Kim, Hyoung-Moon
    • The Korean Journal of Applied Statistics
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    • v.22 no.4
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    • pp.879-891
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    • 2009
  • We developed the projected l-axial skew-normal(LASN) family of distributions for I-axial data. The LASN family of distributions contains the semicircular skew-normal(SCSN) and the circular skew-normal(CSN) families of distributions as special cases. The LASN densities are similar to the wrapped skew-normal densities for the small values of the scale parameter. However CSN densities have more heavy tails than those of the wrapped skew-normal densities on the circle. Furthermore the CSN densities have two modes as the scale parameter increases. The LASN distribution has very convenient mathematical features. We extend the LASN family of distributions to a bivariate case.

Modelling on Multi-modal Circular Data using von Mises Mixture Distribution

  • Jang, Young-Mi;Yang, Dong-Yoon;Lee, Jin-Young;Na, Jong-Hwa
    • Communications for Statistical Applications and Methods
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    • v.14 no.3
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    • pp.517-530
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    • 2007
  • We studied a modelling process for unimodal and multimodal circular data by using von Mises and its mixture distribution. In particular we suggested EM algorithm to find ML estimates of the mixture model. Simulation results showed the suggested methods are very accurate. Applications to two kinds of real data sets are also included.

Spatio-Temporal Variability Analysis of Precipitation Data Through Circular Statistics (순환통계 분석을 통한 강수량 시계열의 시공간적 변동성 분석)

  • Kwon, Hyun-Han;Lee, Jeong-Ju
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.30 no.2B
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    • pp.191-198
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    • 2010
  • Assessing seasonality of precipitation is necessarily required to establish future plans and policies for water resources management. In this regard, a main objective of the study is to introduce an effective approach for assessing the seasonality of the precipitation and evaluate the seasonality through the proposed one. We have used circular statistics to characterize the seasonality on the precipitation in Korea. The circular statistics allow us to effectively assess changes in timing of the seasonality in detail. It was found that peak time on monthly rainfall occurred between end of June and early July in southern coastal area while the timing was delayed in northern part of Korea because of monsoon moving in from south to north. In case of annual daily peak precipitation, spatio-temporal variation of the peak time was increased. It is mainly because of geophysical effects, frequency and paths of typhoons. Finally, temporal variations on the timing of the peak seasons were evaluated through circular statistics by 30-year moving average data. The peak season in the Northen part of Korea (e.g. Seoul and Gangrung) has been moved back from early July to end of July while the peak season has been moved up from middle of July to early July in the Southern part of Korea (e.g. Busan and Mokpo). It seems that changes in seasonality are mostly modulated by variability in the east-asia monsoon system.