This work deals with a generalization of the Total Least Squares method in the context of the functional linear model. We first propose a smoothing splines estimator of the functional coefficient of the model without noise in the covariates and we obtain an asymptotic result for this estimator....

Scalar-on-function regression problems with continuous outcomes arise naturally in many settings, and a wealth of estimation methods now exist. Despite the clear differences in regression model assumptions, tuning parameter selection, and the incorporation of functional structure, it remains...

Findings from previous studies indicate that the long-run stationarity of the real exchange rate in different time horizons remains unclear. In order to shed light on this problem, we have adopted a new method which is widely used to analyze signals, the so-called wavelet transformation. This...

Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to...

We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010-2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among...

Purpose This paper aims to propose an innovative approach to risk measurement for the abolition of selection bias arising from the specious selection of different horizons for investment and risk computation of equity-linked-saving schemes (ELSS). Design/methodology/approach ELSS has a lock-in...

В статье произведен анализ поведения фондовых индексов и курсов валют до и во время кризисных явлений с целью выявления ключевых признаков предкризисного...

Predicting the wind speed at multiple time points over a time span between two and 4 h typically requires a multi-input/multi-output model. This study investigates a wind speed forecasting method based on spectral clustering (SC) and echo state networks (ESNs). A wavelet transformation was used...

<Para ID="Par1">High dimensional data sets are now frequently encountered in many scientific fields. In order to select a sparse set of predictors that have predictive power and/or provide insightful understanding on which predictors really influence the response, a preliminary variable screening is typically...</para>

Artificial neural network is a powerful tool in the forecast of solar irradiance. In order to gain higher forecasting accuracy, artificial neural network and wavelet analysis have been combined to develop a new method of the forecast of solar irradiance. In this paper, the data sequence of solar...