Parametric conditional mean inference with functional data applied to lifetime income curves

TL;DR: In this paper, a methodology for testing a polynomial model hypothesis by extending the approach and results of Baek, Cho, and Phillips (2015; Journal of Econometrics; BCP) that tests for neglected nonlinearity using power transforms of regressors against arbitrary non-linearity is presented.

TL;DR: In this article, the authors study the properties of functional regression and its properties in testing the hypothesis of a constant zero mean function or an unknown constant non-zero mean function, and show that the associated Wald test statistics have standard chi-square limiting null distributions, standard non-central Chi-square distributions for local alternatives converging to zero at p n rate, and are consistent against global alternatives.

TL;DR: This study examined the extreme learning machine (ELM) applied to the Wald test statistic for the model specification of the conditional mean and applied the WELM testing procedure to the sequential testing procedure formed by a set of polynomial models and estimate an approximate conditional expectation.

TL;DR: In this paper, the authors augment standard log earnings equations for workers in US manufacturing with variables reflecting measured and unmeasured attributes of their employer, and find that establishment-level employment, education of coworkers, capital equipment per worker, and firm-level R&D intensity affects earnings substantially.

TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.

TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.

TL;DR: The prime object of as discussed by the authors is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.

TL;DR: Stochastic processes are probabilistic models of data streams such as speech, audio and video signals, stock market prices, and measurements of physical phenomena by digital sensors such as medical instruments, GPS receivers, or seismographs that are essential for understanding phenomena and processing information.