This paper deals with the conditional hazard estimator of a real response where the variable is given a functional random variable (i.e it takes values in an infinite-dimensional space). Specifically, we focus on the functional index model. This approach offers a good com- promise between...

This paper deals with the conditional hazard estimator of a real response where the variable is given a functional random variable (i.e it takes values in an infinite-dimensional space). Specifically, we focus on the functional index model. This approach offers a good com- promise between...

The objective of this dissertation is to develop a suitable statistical methodologyfor functional data analysis. Modern advanced technology allows researchers to collectsamples as functional which means the ideal unit of samples is a curve. We considereach functional observation as the resulting...

We consider a varying coefficient regression model for sparse functional data, with time varying response variable depending linearly on some time independent covariates with coefficients as functions of time dependent covariates. Based on spline smoothing, we propose data driven simultaneous...

High-frequency data can provide us with a quantity of informa- tion for forecasting, help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by ex- ogenous components and may be modelled conditional on other vari- ables. However, many of these...

A functional ARMA-GARCH model for predicting the value-at-risk of the EURUSD exchange rate is introduced. The model implements the yield curve differentials between EUR and the US as exogenous factors. Functional principal component analysis allows us to use the information of basically the...

We propose a statistical procedure to determine the dimension of the nonstationary subspace of cointegrated functional time series taking values in the Hilbert space of square-integrable functions defined on a compact interval. The procedure is based on sequential application of a proposed test...

Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of high-dimensional data. However, in many applications such as risk quantification in finance or climatology, one is interested in capturing the tail variations rather than variation around the mean. In...

Abstract We consider the problem of maximizing the utility of consumption and terminal wealth in a geometric Ornstein–Uhlenbeck market. We calculate the optimal consumption and wealth processes for power, logarithmic and exponential utility as well as their behavior depending e.g. on...

This paper addresses a linguistically motivated question of classification of functional data, namely the statistical classification of languages according to their rhythmic features. This is an important open problem in phonology. The analysis is based on the information provided by the...