This paper aims to investigate a semi-functional partial linear regression model in the presence of missing data in the response variable under the missing at random mechanism. We construct estimators using the kNN-local linear method and establish the asymptotic distribution of the parametric component. Additionally, the uniform almost complete consistency rates for the nonparametric component with respect to the number of neighbors under appropriate conditions is derived. Through simulations and real data analysis, we assess the effectiveness of the proposed approach and demonstrate its superiority by comparing it with existing methods for semi-functional partial linear regression models.

Analyzing the real impact of spatial dependency in financial time series data is crucial to financial risk management. It has been a challenging issue in the last decade. This is because most financial transactions are performed via the internet and the spatial dependency between different international stock markets is not standard. The present paper investigates functional expectile regression as a spatial financial risk model. Specifically, we construct a nonparametric estimator of this functional model for the functional single index regression (FSIR) structure. The asymptotic properties of this estimator are elaborated over general spatial settings. More precisely, we establish Borel-Cantelli consistency (BCC) of the constructed estimator. The latter is obtained with the precision of the convergence rate. A simulation investigation is performed to show the easy applicability of the constructed estimator in practice. Finally, real data analysis about the financial data (Euro Stoxx-50 index data) is used to illustrate the effectiveness of our methodology.

The study of the generalized $t$-deformation of free convolution from the lens of Cauchy–Stieltjes kernel (CSK) families provides an excellent mathematical framework for understanding noncommutative probability distributions. In this paper, we use the concept of generalized $t$-deformation to demonstrate different elements of the Marchenko–Pastur, free Gamma, and inverse semicircle measures in the CSK families setting. These findings advance our knowledge of generalized $t$-deformation in the non-commutative probability framework.