Due to its rapid convergence rate and low memory requirements, the conjugate gradient (CG) scheme is a popular technique for solving unconstrained optimization problems. This paper introduces a modified conjugate grad...
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Due to its rapid convergence rate and low memory requirements, the conjugate gradient (CG) scheme is a popular technique for solving unconstrained optimization problems. This paper introduces a modified conjugate gradient method, GICY. By utilizing the strong Wolfe line search (SWLS), we prove that GICY satisfies the sufficient descent condition at every iteration, guaranteeing significant progress towards the minimum. Additionally, we establish the global convergence of the proposed method. Extensive numerical computations demonstrate the success and promise of this new algorithm, particularly when applied to practical problems involving nonparametric regression functions.
This paper is concerned with a nonparametric estimator of the regression function based on the local linear estimation method in a twice censoring setting. The proposed method avoid the problem of boundary effect and ...
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This paper is concerned with a nonparametric estimator of the regression function based on the local linear estimation method in a twice censoring setting. The proposed method avoid the problem of boundary effect and reduces the bias term. Under suitable assumptions, the strong uniform almost sure consistency with rate is established and the finite sample properties of the local linear regression smoother is investigated by means of a simulation study.
This paper studies the financial risk management of resource allocation of listed commercial banks in my country, starting from the fundamental influence of lender credit rating in the internal rating system of commer...
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This paper studies the financial risk management of resource allocation of listed commercial banks in my country, starting from the fundamental influence of lender credit rating in the internal rating system of commercial banks, and analyzes the influence of a large number of subjective factors contained in the bank's credit rating system on the rating effect. Influence. A sort-response panel regression function model with random effects (including random intercept terms and random coefficients) is proposed, and the rationality of the existing bank credit rating system is tested and analyzed. The experimental results show that the model's prediction accuracy rate is 48.27%, and the percentage of prediction level error within two levels reaches 86.8%. The application of this model can find the redundant indicators of the existing system, which are highly fitted with the actual situation and can achieve quite ideal forecasting results, and provide a forecasting reference for the weight setting, which provides a basis for the revision of the bank's credit rating system.
This paper is about nonparametric regression function estimation. Our estimator is a one-step projection estimator obtained by least-squares contrast minimization. The specificity of our work is to consider a new mode...
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This paper is about nonparametric regression function estimation. Our estimator is a one-step projection estimator obtained by least-squares contrast minimization. The specificity of our work is to consider a new model selection procedure including a cutoff for the underlying matrix inversion, and to provide theoretical risk bounds that apply to non-compactly supported bases, a case which was specifically excluded of most previous results. Upper and lower bounds for resulting rates are provided.
This paper addresses the semiparametric estimation of the regression function in a situation where the response variable is right-censored and the covariate(s) is completely observed. We present a new copula-based met...
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This paper addresses the semiparametric estimation of the regression function in a situation where the response variable is right-censored and the covariate(s) is completely observed. We present a new copula-based method to estimate the regression function. The key concept presented in this manuscript is to write the regression function in terms of the copula density and marginal distributions. We suppose a parametric model for the copula density with unknown parameter(s), and we estimate the marginal distributions of the response and the covariate(s) by the Kaplan-Meier estimator and the empirical distribution, respectively. We establish the asymptotic properties of our estimator and extend it to the multivariate case. The proposed method is then applied to analyse a data-set on lifetime with lung-cancer.
This paper deals with the problem of nonparametric relative error regression function estimation for twice censored data. A new estimate is proposed, which is built by combining the local linear approach and relative ...
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This paper deals with the problem of nonparametric relative error regression function estimation for twice censored data. A new estimate is proposed, which is built by combining the local linear approach and relative error estimation. A uniform almost sure consistency with rate over a compact set is established. A numerical study is carried out to assess the performance of the proposed estimator. Practical results indicate the robustness of the new estimate compared to other existing estimators in the presence of censored and outliers datum. (C) 2021 Elsevier B.V. All rights reserved.
We are mainly concerned with the partially linear additive model defined for a measurable function psi : R-q -> R, by [Graphics] where (Z(i) = Z(1,i),...,Z(p,i))(T) and X-i=(X-1,X-i,...,X-i,(d))(T )are vectors of e...
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We are mainly concerned with the partially linear additive model defined for a measurable function psi : R-q -> R, by [Graphics] where (Z(i) = Z(1,i),...,Z(p,i))(T) and X-i=(X-1,X-i,...,X-i,(d))(T )are vectors of explanatory variables, beta=(beta(1), horizontexpressionl ellipsis ,beta(p))(?) is a vector of unknown parameters, m(1),..., m(d) are unknown univariate real functions, and epsilon(1), ...,epsilon(n) are independent random errors with mean zero, finite variances re and E(epsilon|X,Z)=0 a.s. Under some mild conditions, we present a sharp uniform-in-bandwidth limit law for the nonlinear additive components of the model estimated by the marginal integration device with the kernel method. We allow the bandwidth to varying within the complete range for which the estimator is consistent. We provide the almost sure simultaneous asymptotic confidence bands for the regression functions.
The focus of this article is on studying a partially linear additive model, which is defined using a measurable function psi:Rq -> R. The model is given as follows: psi(Yi):=Yi=ZiT beta+ n-ary sumation l=1dml(Xl,i)...
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The focus of this article is on studying a partially linear additive model, which is defined using a measurable function psi:Rq -> R. The model is given as follows: psi(Yi):=Yi=ZiT beta+ n-ary sumation l=1dml(Xl,i)+epsilon ifor1 <= i <= n, where Zi=(Zi,1, horizontal ellipsis ,Zip)T and Xi=(X1,i, horizontal ellipsis ,Xid)T are vectors of explanatory variables, beta=(beta 1, horizontal ellipsis ,beta p)Tis a vector of unknown parameters, m1, horizontal ellipsis ,md are unknown univariate real functions, and epsilon 1, horizontal ellipsis ,epsilon n are independent random errors with mean zero, finite variances sigma epsilon. Additionally, it is assumed that E(epsilon|X,Z)=0 almost surely. The main contributions of this article are as follows. First, under certain mild conditions, we establish the asymptotic normality of the non linear additive components of the model. These components are estimated using the marginal integration device with the linear wavelet method. Second, we leverage our main result to construct confidence intervals for the estimated model.
In this paper, we introduce a new kernel functional regression estimator when the random response variable is subject to twice censoring. Then, we establish the asymptotic normality of our estimator and we deduce the ...
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In this paper, we introduce a new kernel functional regression estimator when the random response variable is subject to twice censoring. Then, we establish the asymptotic normality of our estimator and we deduce the asymptotic confidence interval of the regression function. To enhance our theoretical results, the performance and the asymptotic Gaussian behavior of our estimator are highlighted through a simulation study and an application to real data.
This article considers an adaptive method based on the relative error criteria to estimate the regression operator by a kernel smoothing. It is assumed that the variable of interest is subject to random right censorin...
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This article considers an adaptive method based on the relative error criteria to estimate the regression operator by a kernel smoothing. It is assumed that the variable of interest is subject to random right censoring and that the observations are from a stationary alpha-mixing process. The uniform almost sure consistency over a compact set with rate where we highlighted the covariance term is established. The simulation study indicates that the proposed approach has better performance in the presence of high level censoring and outliers in data to an existing classical method based on the least squares. An experiment prediction shows the quality of the relative error predictor.
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