In financial econometrics, the Sharpe ratio function serves as a gold standard to measure return-to-risk for comparing different assets or trading strategies. recent literature, several methods have been developed directly indirectly estimate function, yet none of them apply scenario where covariates are measured with error. To handle this problem, we propose new method by incorporating local polynomial smoothing and SIMEX simultaneously negative log-volatility in presence measurement The...

Single index models are natural extensions of linear and overcome the so-called curse dimensionality. They very useful for longitudinal data analysis. In this paper, we develop a new efficient estimation procedure single with data, based on Cholesky decomposition local smoothing method. Asymptotic normality proposed estimators both parametric nonparametric parts will be established. Monte Carlo simulation studies show excellent finite sample performance. Furthermore, illustrate our methods...

We consider quantile functional regression with a part and scalar linear part. establish the optimal prediction rate for model under mild assumptions in reproducing kernel Hilbert space (RKHS) framework. Under stronger related to capacity of RKHS, non-functional is shown have asymptotic normality. The estimators are illustrated simulation studies.

Abstract With the expansion of epidemic, online multimedia teaching has become a common trend. The reasoning model evaluation is useful tool to infer result effects and predict tendency. However, ambiguity in linguistic-valued leads problems always context with uncertainty. To make better deal multiple multidimensional uncertainty environment, while considering both positive evidence negative at same time, this paper mainly focuses on linguistic truth-valued intuitionistic fuzzy layered...

Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality the proposed estimators of functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. illustrate our methods with real example.

Nonparametric regression has been widely used to deal with nonlinearity and heteroscedasticity in financial time series. As the ratio of mean standard deviation functions, Sharpe function is one most commonly risk/return measures econometrics. Most existing methods take an indirect procedure, which first estimates variance functions then applies these two estimate function. In practice, however, such procedure can often be less efficient. this article, we propose a direct method by local...

In high-dimensional generalized linear models, it is crucial to identify a sparse model that adequately accounts for response variation. Although the best subset section has been widely regarded as Holy Grail of problems this type, achieving either computational efficiency or statistical guarantees challenging. article, we intend surmount obstacle by utilizing fast algorithm select with high certainty. We proposed and illustrated an recovery in regularity conditions. Under mild conditions,...

The explosion of large-scale data in fields such as finance, e-commerce, and social media has outstripped the processing capabilities single-machine systems, driving need for distributed statistical inference methods. Traditional approaches to often struggle with achieving true sparsity high-dimensional datasets involve high computational costs. We propose a novel, two-stage, best subset selection algorithm address these issues. Our approach starts by efficiently estimating active set while...

In this note, we consider the situation where have a functional predictor as well some more traditional scalar predictors, which call partially problem. We propose semiparametric model based on sufficient dimension reduction, and thus our main interest is in reduction although prediction can be carried out at second stage. establish root-n consistency of linear part estimator. Some Monte Carlo studies are proof concept.

In haemodialysis patients, vascular access type is of paramount importance. Although recent studies have found that central venous catheter often associated with poor outcomes and switching to arteriovenous fistula beneficial, not fully elucidated how the effect on changes over time for patients dialysis whether depends time. this paper, we characterise patients. This achieved by using a new class multiple-index varying-coefficient (MIVC) models. We develop estimation procedure MIVC models...

Matrix regression provides a powerful technique for analyzing matrixtype data, as exemplified by many contemporary applications.Despite the rapid advance, distributed learning robust matrix to deal with heavytailed noises in big data regime still remains untouched.In this paper, we first consider adaptive Huber nuclear norm penalty, which enjoys insensitivity heavy-tailed without losing statistical accuracy.To further enhance scalability massive applications, employ communication-efficient...