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A General Framework for Quantile Estimation with Incomplete Data [PDF]
J R Stat Soc Series B Stat Methodol, 2019 Peisong Han +3 more
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Kernel Quantile Estimators [PDF]
Journal of the American Statistical Association, 1990 Abstract For an estimator of quantiles, the efficiency of the sample quantile can be improved by considering linear combinations of order statistics, that is, L estimators. A variety of such methods have appeared in the literature; an important aspect of this article is that asymptotically several of these are shown to be kernel estimators with a ...
Simon J. Sheather, J. S. Marron
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Estimation for Extreme Conditional Quantiles of Functional Quantile Regression
Statistica Sinica, 2023 Quantile regression as an alternative to modeling the conditional mean function provides a comprehensive picture of the relationship between a response and covariates. It is particularly attractive in applications focused on the upper or lower conditional quantiles of the response. However, conventional quantile regression estimators are often unstable
Zhu, Hanbing +3 more
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Simulation Study The Implementation of Quantile Bootstrap Method on Autocorrelated Error
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2018 Quantile regression is a regression method with the approach of separating or dividing data into certain quantiles by minimizing the number of absolute values from asymmetrical errors to overcome unfulfilled assumptions, including the presence of ...
Ovi Delviyanti Saputri +2 more
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Nonparametric Limits of Agreement for Small to Moderate Sample Sizes: A Simulation Study
Stats, 2020 The assessment of agreement in method comparison and observer variability analysis of quantitative measurements is usually done by the Bland–Altman Limits of Agreement, where the paired differences are implicitly assumed to follow a normal distribution ...
Maria E. Frey +2 more
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Some Reliability Properties of Extropy and its Related Measures Using Quantile Function
Statistica, 2021 Extropy is a recent addition to the family of information measures as a complementary dual of Shannon entropy, to measure the uncertainty contained in a probability distribution of a random variable.
Aswathy Sree Krishnan +2 more
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Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement
Journal of Probability and Statistics, 2021 In this paper, we propose an extreme conditional quantile estimator. Derivation of the estimator is based on extreme quantile autoregression. A noncrossing restriction is added during estimation to avert possible quantile crossing.
Martin M. Kithinji +2 more
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Functional coefficient quantile regression model with time-varying loadings
Journal of Applied Economics, 2023 This paper proposes a functional coefficient quantile regression model with heterogeneous and time-varying regression coefficients and factor loadings. Estimation of the model coefficients is done in two stages.
Alev Atak +2 more
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Mathematics, 2023 Space-time varying coefficient models, which are used to identify the effects of covariates that change over time and spatial location, have been widely studied in recent years. One such model, called the quantile regression model, is particularly useful
Bertho Tantular +3 more
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High Quantile Estimation and the Port Methodology
Revstat Statistical Journal, 2009 In many areas of application, a typical requirement is to estimate a high quantile χ1−p of probability 1−p, a value, high enough, so that the chance of an exceedance of that value is equal to p, small.
Lígia Henriques-Rodrigues +1 more
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