This research considers the ranking and selection (R&S) problem of selecting the optimal subset from a finite set of alternative designs. Given the t…

We propose a new method for estimating the extreme quantiles for a function of several dependent random variables. In contrast to the conventional ap…

This paper presents a framework of developing neural networks for predicting implied volatility surfaces. Conventional financial conditions and empir…

The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with expos…

In this paper, we propose a gated deep neural network model to predict implied volatility surfaces. Conventional financial conditions and empirical e…

This paper considers Bayesian multiple testing under sparsity for polynomial-tailed distributions satisfying a monotone likelihood ratio property. In…

While it is common practice in applied network analysis to report various standard network summary statistics, these numbers are rarely accompanied b…

In many applications, it makes sense to solve the least square problems with nonnegative constraints. In this article, we present a new multiplicativ…

Precision matrices play important roles in many practical applications. Motivated by temporally dependent multivariate data in modern social and scie…

The aim of this paper is to study the fast computation of the lower and upper bounds on the value function for utility maximization under the Heston …

In this paper, We study an one--dimensional morphogenesis model considered by C. Stinner et al. in (Math. Meth. Appl. Sci. 2012,35 (445-465). Under h…

In this letter, we proposed a construction of binary sequences of period 4p with the interleaved structure

Binary sequences with optimal autocorrelation play important roles in radar, communication, and cryptography. Finding new binary sequences with optim…

Knowledge graph (KG) refinement mainly aims at KG completion and correction (i.e., error detection). However, most conventional KG embedding models o…

In this paper, we study a fast and linearized finite difference method to solve the nonlinear time-fractional wave equation with multi fractional ord…

We study a diffusive Lotka-Volterra competition system with advection under Neumann boundary conditions. Our system models a competition relationship…

Variable selection is central to high-dimensional data analysis, and various algorithms have been developed. Ideally, a variable selection algorithm …

In this paper we study a utility maximization problem with both optimal control and optimal stopping in a finite time horizon. The value function can…

In this paper, we consider efficiently learning the structural information from the highdimensional noise in high-frequency data via estimating its c…

This paper presents a framework of developing neural networks to predict implied volatility surfaces. It can incorporate the related properties from …

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