Abstract Along the lines of Janssen's and Pfanzagl's work the testing theory for statistical functionals is further developed for non-parametric one-sample problems. Efficient tests for the one-sided and two-sided problems are derived for nonparametric statistical functionals. The asymptotic...

Results of assessments of total labor factor productivity TFP showed improvements where it recorded higher rate of 73.5% compared to previous periods. However, that was related with constant rate of returns as compared with variable rate of return. When technical efficiency was compared with...

Let (X1, Y1), ..., (Xn, Yn) be i.i.d. rvs and let v(x) be the unknown T-expectile regression curve of Y conditional on X. An expectile-smoother vn(x) is a localized, nonlinear estimator of v(x). The strong uniform consistency rate is established under general conditions. In many applications it...

Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The...

In semiparametric models it is a common approach to under-smooth the nonparametric functions in order that estimators of the finite dimensional parameters can achieve root-n consistency. The requirement of under-smoothing may result as we show from inefficient estimation methods or technical...

Gini coefficient is a Statistical measure for Income Distribution. This paper is an exposition of data concerning the Gini Coefficients for the Greek Income Tax Service for the years 1960 to 2000. Using von Mises Expansions we find second-order properties of Gini Coefficient. We estimate its...

We consider a new procedure for detecting structural breaks in mean for high-dimensional time series. We target breaks happening at unknown time points and locations. In particular, at a fixed time point our method is concerned with either the biggest break in one location or aggregating...

Among non-parametric smoothers, there is a well-known correspondence between kernel and Fourier series methods, pivoted by the Fourier transform of the kernel. This suggests a similar relationship between kernel and spline estimators. A known special case is the result of Silverman (1984) on the...

Function approximation is at the heart of machine learning. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning...

Accounting journals regularly include articles where regression coefficients are estimated and reported. However, the level of accuracy with which the estimated coefficients are reported is often not consistent within a single paper. Furthermore, in almost all cases, the level of accuracy...