Results 231 to 240 of about 73,349 (260)

Quantile Function Models

2013
One of the objectives of quantile-based reliability analysis is to make use of quantile functions as models in lifetime data analysis. Accordingly, in this chapter, we discuss the characteristics of certain quantile functions known in the literature. The models considered are the generalized lambda distribution of Ramberg and Schmeiser, the generalized
N. Unnikrishnan Nair, P. G. Sankaran, N. Balakrishnan   +2 more
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Functional quantile autoregression

Journal of Econometrics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dong, Chaohua, Chen, Rong, Xiao, Zhijie, Liu, Weiyi   +3 more
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Estimation of the Quantile Function of an IFRA Distribution

Scandinavian Journal of Statistics, 1998
Let F and G be lifetime distributions and consider the problem of estimating F−1 when it is known that G−1F is star‐shaped. Estimators of F−1 are considered here which are shown to be uniformly strongly consistent. The case of censored data is also presented. Asymptotic confidence intervals and bands for F−1 are provided. The result are applicable, for
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AN AXIOMATIZATION OF QUANTILES ON THE DOMAIN OF DISTRIBUTION FUNCTIONS

Mathematical Finance, 2009
In an environment in which the primitive is the space of distribution functions, we characterize the quantile functions by the axioms ordinal covariance, monotonicity with respect to first‐order stochastic dominance, and upper semicontinuity. We show how to characterize the VaR in a similar manner.
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Data Modeling Using Quantile and Density-Quantile Functions.

1980
Abstract : Statistical data modeling is a field of statistical reasoning that seeks to fit models to data without using models based on prior theory; rather one seeks to learn the model by a process which could be called statistical model identification.
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Multivariate Quantile Impulse Response Functions

Journal of Time Series Analysis, 2019
A reduced form multivariate quantile autoregressive model is developed to study heterogeneity in the effects of macroeconomic shocks. This framework is used for forecasting and for constructing quantile impulse response functions that explore dynamic heterogeneity in the response of endogenous variables to different shocks.
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The bilinear mean residual quantile function

Communications in Statistics - Theory and Methods, 2023
P G Sankaran, S M Sunoj
exaly  

A Quantile Function Approach to the K-Sample Quantile Regression Problem.

1980
Abstract : A procedure for estimating the parameters of a quantile regression function is investigated. The procedure is based on the work of Parzen (1979a) in the theory of quantile functions and is applicable to a wide range of distributional families.
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Quantile Functions

2013
N. Unnikrishnan Nair, P. G. Sankaran, N. Balakrishnan   +2 more
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Functional Quantiles

2011
Ricardo Fraiman, Beatriz Pateiro-López
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