Estimation of a Regression Function on a Point Process and its Application to Financial Ruin Risk Forecast

Dia, Galaye; Kone, Abdoulaye

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Hazard rate model and statistical analysis of a compound point process

Petr Volf (2005)

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A stochastic process cumulating random increments at random moments is studied. We model it as a two-dimensional random point process and study advantages of such an approach. First, a rather general model allowing for the dependence of both components mutually as well as on covariates is formulated, then the case where the increments depend on time is analyzed with the aid of the multiplicative hazard regression model. Special attention is devoted to the problem of prediction of process...

Empirical regression quantile processes

Jana Jurečková, Jan Picek, Martin Schindler (2020)

Applications of Mathematics

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We address the problem of estimating quantile-based statistical functionals, when the measured or controlled entities depend on exogenous variables which are not under our control. As a suitable tool we propose the empirical process of the average regression quantiles. It partially masks the effect of covariates and has other properties convenient for applications, e.g. for coherent risk measures of various types in the situations with covariates.

Analysis of generalized residuals in hazard regression models

Petr Volf (1996)

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Generalized regression estimation for continuous time processes with values in functional spaces

Bertrand Maillot, Christophe Chesneau (2021)

Commentationes Mathematicae Universitatis Carolinae

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We consider two continuous time processes; the first one is valued in a semi-metric space, while the second one is real-valued. In some sense, we extend the results of F. Ferraty and P. Vieu in ``Nonparametric models for functional data, with application in regression, time-series prediction and curve discrimination'' (2004), by establishing the convergence, with rates, of the generalized regression function when a real-valued continuous time response is considered. As corollaries, we...

Aspects of analysis of multivariate failure time data.

Ross L. Prentice, John D. Kalbfleisch (2003)

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Multivariate failure time data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated failure time when an individual is followed for the occurrence of two or more types of events for which the individual is simultaneously at risk, or when distinct individuals have depending event times; or more complicated multistate processes where individuals may move among a number of discrete...

Blind deconvolution for jump-preserving curve estimation.

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Smoothing dichotomy in randomized fixed-design regression with strongly dependent errors based on a moving average

Artur Bryk (2014)

Applicationes Mathematicae

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We consider a fixed-design regression model with errors which form a Borel measurable function of a long-range dependent moving average process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We show that the Priestley-Chao kernel estimator of the regression fuction exhibits a dichotomous asymptotic behaviour depending on the amount of smoothing employed. Moreover, the resulting estimator is...

Formalisation and methods of analysis of the fast xenobiotic mass transfer in the body

Volodymir G. Zinkovsky, Olga V. Zhuk, Michał Teodorczyk, Natalia Karpinchik (2009)

Applicationes Mathematicae

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A novel discrimination and regression method for a quantitative determination of the relative efficiency of "fast" distribution processes of xenobiotics is discussed. An integral model-independent method for estimation of equilibrium tissue-to-plasma partition ratios is proposed.

On the Lp-Norm Regression Models for Estimating Value-at-Risk

Kumar, Pranesh, Kashanchi, Faramarz (2014)

Serdica Journal of Computing

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Analysis of risk measures associated with price series data movements and its predictions are of strategic importance in the financial markets as well as to policy makers in particular for short- and longterm planning for setting up economic growth targets. For example, oilprice risk-management focuses primarily on when and how an organization can best prevent the costly exposure to price risk. Value-at-Risk (VaR) is the commonly practised instrument to measure risk and is evaluated...