Estimation of Kendall’s tau from censored data

Hsieh, Jin-Jian

doi:10.1016/j.csda.2010.01.015

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…In order to define the Kendall rank correlation coefficient, a less formal definition of compatibility should first be introduced, as follows [46].…”

Section: Kendall's Correlation Coefficientmentioning

confidence: 99%

Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function

Gródek-Szostak

Malik²,

Kajrunajtys

et al. 2019

Sustainability

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The purpose of the article is to identify and estimate the dependency model for the extreme prices of agricultural products listed on the Chicago Mercantile Exchange. The article presents the results of the first stage of research covering the time interval 1975–2010. The selected products are: Corn, soybean and wheat. The analysis of the dependency between extreme price values on the selected futures was based on the estimation of five models of two-dimensional extreme value copulas, namely, the Galambos copula, the Gumbel copula, the Husler–Reiss copula, the Tawn asymmetric copula and the t-EV copula. The next stage of the analysis was to test whether the structure of the dependency described with the estimated copulas is a sufficient approximation of reality, and whether it is suitable for modeling empirical data. The quality of matching the estimated copulas to empirical data of return rates of agricultural products was assessed. For this purpose, the Kendall coefficient was calculated, and the methodology of the empirical combining function was used. The conducted research allowed for the determination of the conduct for this kind of phenomena as it is crucial in the process of investing in derivatives markets. The analyzed phenomena are highly dependent on e.g., financial crises, war, or market speculation but also on drought, fires, rainfall, or even crop oversupply. The conducted analysis is of key importance in terms of balancing agricultural production on a global scale. It should be emphasized that conducting market analysis of agricultural products at the Chicago Mercantile Exchange in the context of competition with the agricultural market of the European Union is of significant importance.

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“…In order to define the Kendall rank correlation coefficient, a less formal definition of compatibility should first be introduced, as follows [46].…”

Section: Kendall's Correlation Coefficientmentioning

confidence: 99%

Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function

Gródek-Szostak

Malik²,

Kajrunajtys

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…For the case of right censored observations, that is, response times that are only known to be higher than an observed lower boundary, specific estimators of Kendall's Tau exist; see W. Wang and Wells (2010) ;Lakhal, Rivest, and Beaudoin (2009);and Hsieh (2010) for an overview. Especially the estimator of Lakhal et al can be recommended for its simplicity and good performance.…”

Section: Estimation Of the Regression Coefficientsmentioning

confidence: 98%

Response Time Modeling Based on the Proportional Hazards Model

Ranger

Ortner

2013

Multivariate Behavioral Research

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Response time data are regularly analyzed in psychology. When several response times are assessed per participant, it is common practice to use latent trait models in order to account for the dependency of the response times from the same participant. One such model is the proportional hazards model with random effects. Despite its popularity in survival analysis, this model is rarely used in psychology because of the difficulty of model estimation when latent variables are present. In this article, a new estimation method is proposed. This method is based on the rank correlation matrix containing Kendall's Tau coefficients and unweighted least squares estimation ( Kendall, 1938 ). Compared with marginal maximum likelihood estimation, the new estimation approach is simple, not computationally intensive, and almost as efficient. Additionally, the approach allows the implementation of a test for model fit. Feasibility of the estimation method and validity of the fit test is demonstrated with a simulation study. An application of the model to a real data set is provided.

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“…Remark 1. There exist a number of different proposals to estimate Kendall's Tau under bivariate censoring, see Lakhal et al (2009) or Hsieh (2010 and references therein. Any of these methods can be used to define a local association measure in the spirit ofτ t , and their relative performance is an interesting issue for future investigation.…”

Section: Measuring Future-past Associationmentioning

confidence: 99%

Testing Markovianity in the three‐state progressive model via future‐past association

Rodríguez-Girondo

Uña‐Álvarez

2012

Biometrical J

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The three-state progressive model is a special multi-state model with important applications in Survival Analysis. It provides a suitable representation of the individual's history when an intermediate event (with a possible influence on the survival prognosis) is experienced before the main event of interest. Estimation of transition probabilities in this and other multi-state models is usually performed through the Aalen-Johansen estimator. However, Aalen-Johansen may be biased when the underlying process is not Markov. In this paper, we provide a new approach for testing Markovianity in the three-state progressive model. The new method is based on measuring the future-past association along time. This results in a deep inspection of the process that often reveals a non-Markovian behaviour with different trends in the association measure. A test of significance for zero future-past association at each time point is introduced, and a significance trace is proposed accordingly. The finite sample performance of the test is investigated through simulations. We illustrate the new method through real data analysis.

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Estimation of Kendall’s tau from censored data

Cited by 12 publications

References 13 publications

Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function

Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function

Response Time Modeling Based on the Proportional Hazards Model

Testing Markovianity in the three‐state progressive model via future‐past association

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