Results 181 to 190 of about 1,350 (213)

Risk preferences on the space of quantile functions

Mathematical Programming, 2013
The recent authors' paper [SIAM J. Optim. 23, No. 1, 381--405 (2013; Zbl 1271.91051)] provided a unified approach to two very popular theories, the theory of expected utility and the dual utility theory, by using tools of modern convex analysis. The aim of the present paper is to continue along the theory of the previous paper to develop a quantile ...
Darinka Dentcheva, Andrzej Ruszczynski
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Evaluating the Quantile and Density Quantile Function

1989
In this chapter (a) we start with the “pure” nonparametric, statistical model, (b) introduce smoothness conditions.
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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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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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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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Partially Adaptive Estimation via Quantile Functions

Communications in Statistics - Simulation and Computation, 2007
The conceptual model “observation = deterministic component + stochastic compo-nent” underlies most uses of regression analysis. In this article, the deterministic part of the model is linear and we propose a new procedure of partially adaptive estimation of its parameters that responds to a broad class of problems occurring in regression analysis ...
TARSITANO, Agostino, PERRI, PIER FRANCESCO   +1 more
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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
Sunoj Sm, P G Sankaran
exaly