Students of introductory courses consider statistics as particularly difficult, as the understanding of the underlying concepts may require more time and energy than for other disciplines. For decades statisticians have tried to enhance understanding with the help of technical solutions such as...

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...

Porter hypothesis states that environmental regulation may lead to win-win opportunities, that is, improve the productivity and reduce the undesirable output simultaneously. Based on directional distance function, this paper proposes a novel dynamic activity analysis model to forecast the...

Let be a strictly stationary real valued time series. We predict ZN + 1 from {Z1,...ZN} by a robust nonparametric method. The predictor is defined by the kernel method and constructed as a functional M-estimate connected with the conditional law of Zp+1 on Z1,...,Zp, when is Markovian of order...

Positive definiteness of income effect matrices provides a sufficient condition for the <italic>law of demand</italic> to hold. Given cross section household expenditure data, empirical evidence for the law of demand can be obtained by estimating such matrices. Härdle, Hildenbrand, and Jerison used the...

Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance...

Expectile regression, as a general M smoother, is used to capture the tail behaviour of a distribution. Let (X <Subscript>1</Subscript>,Y <Subscript>1</Subscript>),…,(X <Subscript> n </Subscript>,Y <Subscript> n </Subscript>) be i.i.d. rvs. Denote by v(x) the unknown τ-expectile regression curve of Y conditional on X, and by v <Subscript> n </Subscript>(x) its kernel smoothing estimator. In this paper, we...</subscript></subscript></subscript></subscript></subscript>