On the construction of a nonlinear recursive predictor

In this paper, we present a novel approach for constructing a nonlinear recursive predictor. Given a limited time series data set, our goal is to develop a predictor that is capable of providing reliable long-term forecasting. The approach is based on the use of an artificial neural network and we propose a combination of network architecture, training algorithm, and special procedures for scaling and initializing the weight coefficients. For time series arising from nonlinear dynamical systems, the power of the proposed predictor has been successfully demonstrated by testing on data sets obtained from numerical simulations and actual experiments.     

Short memory principle and a predictor–corrector approach for fractional differential equations

Fractional differential equations are increasingly used to model problems in acoustics and thermal systems, rheology and modelling of materials and mechanical systems, signal processing and systems identification, control and robotics, and other areas of application. This paper further analyses the underlying structure of fractional differential equations. From a new point of view, we apprehend the short memory principle of fractional calculus and farther apply a Adams-type predictor–corrector approach for the numerical solution of fractional differential equation. And the detailed error analysis is presented. Combining the short memory principle and the predictor–corrector approach, we gain a good numerical approximation of the true solution of fractional differential equation at reasonable computational cost. A numerical example is provided and compared with the exact analytical solution for illustrating the effectiveness of the short memory principle.     

On zero locations of predictor polynomials

Predictor polynomials are often used in linear prediction methods mainly for extracting properties of physical systems which are described by time series. The aforementioned properties are associated with a few zeros of large polynomials and for this reason the zero locations of those polynomials must be analyzed. We present a linear algebra approach for determining the zero locations of predictor polynomials, which enables us to generalize some early results obtained by Kumaresan in the signal analysis field. We also present an analysis of zero locations for time series having multiple zeros. © 1997 by John Wiley & Sons, Ltd.     

Sufficient dimension reduction for the conditional mean with a categorical predictor in multivariate regression

Recent sufficient dimension reduction methodologies in multivariate regression do not have direct application to a categorical predictor. For this, we define the multivariate central partial mean subspace and propose two methodologies to estimate it. The first method uses the ordinary least squares. Chi-squared distributed statistics for dimension tests are constructed, and an estimate of the target subspace is consistent and efficient. Moreover, the effects of continuous predictors can be tested without assuming any model. The second method extends Iterative Hessian Transformation to this context. For dimension estimation, permutation tests are used. Simulated and real data examples for illustrating various properties of the proposed methods are presented.     

Dimension reduction in functional regression with categorical predictor

In the present paper, we consider dimension reduction methods for functional regression with a scalar response and the predictors including a random curve and a categorical random variable. To deal with the categorical random variable, we propose three potential dimension reduction methods: partial functional sliced inverse regression, marginal functional sliced inverse regression and conditional functional sliced inverse regression. Furthermore, we investigate the relationships among the three methods. In addition, a new modified BIC criterion for determining the dimension of the effective dimension reduction space is developed. Real and simulation data examples are then presented to show the effectiveness of the proposed methods.     

Computational experience with a globally convergent primal—dual predictor—corrector algorithm for linear programming

Kojima, Megiddo, and Mizuno proved global convergence of a primal—dual algorithm that corresponds to methods used in practice. Here, the numerical efficiency of a predictor—corrector extension of that algorithm is tested. Numerical results are extremely positive, indicating that the safety of a globally convergent algorithm can be obtained at little computational cost. The algorithm is tested on infeasible problems with less success. Finally, the algorithm is applied to a warm started problem, with very encouraging preliminary results.Corresponding author. The research of this author is sponsored by the Air Force Office of Scientific Research, Air Force System Command under Grant AFOSR-92-J0046. The United States Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notations thereon.     

中心静脉血氧饱和度对心肺复苏后血流动力学不稳定患者容量反应性的预测价值

目的 分析影响子宫腺肌病发病的影响因素,寻找子宫腺肌病的防治及干预措施,从而减少子宫腺肌病的发病风险。方法 采用病例对照研究的方法,以128 例患者为实验组,同期入院的非子宫腺肌病的患者256 例为对照组。收集可能影响子宫腺肌病的影响因素,包括一般情况、生育及月经情况、生物学指标情况。采用SPSS18.0 统计软件进行统计分析,先行单因素因素分析,根据单因素分析有统计学差异的因素进行多因素logistic 回归分析。结果 单因素分析发现年龄、职业、早产、人工流产次数、剖宫产、痛经、ER、PR 和CA125 阳性为危险因素。多因素logistic 回归分析证实年龄、职业、早产、剖宫产、人工流产次数、FR 阳性、ER 阳性和CA125 阳性为子宫腺肌病的影响因素。结论 年龄、职业、早产、剖宫产、人工流产次数、FR 阳性、ER 阳性和CA125阳性为子宫腺肌病的影响因素,对其危险因素进行干预,可为防治方式提供参考。     

Local linear regression for functional predictor and scalar response

The aim of this work is to introduce a new nonparametric regression technique in the context of functional covariate and scalar response. We propose a local linear regression estimator and study its asymptotic behaviour. Its finite-sample performance is compared with a Nadayara-Watson type kernel regression estimator and with the linear regression estimator via a Monte Carlo study and the analysis of two real data sets. In all the scenarios considered, the local linear regression estimator performs better than the kernel one, in the sense that the mean squared prediction error is lower.     

正态总体中线性可预测变量的Minimax预测

在二次损失下研究了任意秩有限总体中线性可预测变量Ay的线性预测在一切预测类中的Minimax性．对于一般随机效应线性模型y=Xβ ε,(ε^β)-N(0^Ba),σ(W′^U V^W)),这里X,B,U,V和W是已知矩阵,a∈R^k和σ^2＞0是未知参数,文中得到了Ay的惟一Minimax预测。     

MSE of the best linear predictor in nonorthogonal models

The problem of computing the mean squared error (MSE) of the best linear predictor (BLP) in finite discrete spectrum with an additive white noise models(FDSWNMs) for an observed time series is considered. This is done under the assumption that the corresponding vectors in models for finite observation of this time series are not orthogonal. Supported by the VEGA grant of the Slovak Grant Agency. No. 1/3023/06.     

Nonlinear least-squares estimates with errors in the predictor variables

Parameter estimators are derived for the nonlinear regression model under various assumptions. The limiting behavior of the estimators is investigated.Translated from Statisticheskie Metody, pp. 19–48, 1982.     

A fundamental relation between predictor identification and power spectrum estimation

Summary An asymptotic linear relation between the FPE (final prediction error) of the predictor obtained by using the least squares estimate of autoreggression coefficients and the integrated relative mean square error of the estimate of power spectrum obtained from the fitted autoregression model is established. This relation provides a link between the predictor identification and the power spectrum estimation and has already been used as a theoretical background of a former paper [3]. The Institute of Statistical Mathematics     

Conjugate gradient predictor corrector method for solving large scale problems

In this paper, we give a new method for solving large scale problems. The basic idea of this method depends on implementing the conjugate gradient as a corrector into a continuation method. We use the Euler method as a predictor. Adaptive steplength control is used during the tracing of the solution curve. We present some of our experimental examples to demonstrate the efficiency of the method.

     

ON TESTING FOR NO EFFECT OF THE PREDICTOR ON RESPONSE

1.Introduction'Givenasequenceofpairdata(yi1ti),'t(aam,t.),',itisimportanttocheckwhetherornotthepredictorvariablethasaneffectontheresponsevariabley,sincethisisessentialinstudyingthebehaviouroftheprocessandpredictingthefuturevalueofy.Alongthisdirection,therearetwokindsofmodels,namely,regressionmodelandtimeseriesmodelwhichareoftenconsidered:i)Regressionmodelyi~p g(ti) fi,i~1,...?n,(1.1)wheretiarefixeddesignedpointsorrandompoilltscorrespondingtovariousconcretesituations.n)Timeseriesmodelyi~P …     

On polynomiality of the Mehrotra-type predictor—corrector interior-point algorithms

Recently, Mehrotra [3] proposed a predictor—corrector primal—dual interior-point algorithm for linear programming. At each iteration, this algorithm utilizes a combination of three search directions: the predictor, the corrector and the centering directions, and requires only one matrix factorization. At present, Mehrotra's algorithmic framework is widely regarded as the most practically efficient one and has been implemented in the highly successful interior-point code OB1 [2]. In this paper, we study the theoretical convergence properties of Mehrotra's interior-point algorithmic framework. For generality, we carry out our analysis on a horizontal linear complementarity problem that includes linear and quadratic programming, as well as the standard linear complementarity problem. Under the monotonicity assumption, we establish polynomial complexity bounds for two variants of the Mehrotra-type predictor—corrector interior-point algorithms. These results are summarized in the last section in a table.Research supported in part by NSF DMS-9102761, DOE DE-FG05-91ER25100 and DOE DE-FG02-93ER25171.Corresponding author.     

A sequential predictor retraining algorithm and its application to market prediction

In many applications, there are a large number of predictors, designed manually or trained automatically, to predict the same outcome. Much research has been devoted to the design of algorithms that can effectively select/combine these predictors to generate a more accurate ensemble predictor. The collaborative training algorithms from attribute distributed learning provide batch-processing solutions for scenarios in which the individual predictors are heterogeneous, taking different inputs and employing different models. However, in some applications, for example financial market prediction, it is desirable to use an online approach. In this paper, an innovative online algorithm is proposed, stemming from the collaborative training algorithms developed for attribute distributed learning. It sequentially takes new observations, simultaneously adjusting the way that the individual predictors are combined, and provides feedback to the individual predictors for them to be retrained in order to achieve a better ensemble predictor in real time. The efficacy of this new algorithm is demonstrated by extensive simulations on both artificial and real data, and particularly for financial market data. A trading strategy constructed from the ensemble predictor shows strong performance when applied to financial market prediction.     

A modified predictor–corrector scheme for the two-dimensional sine-Gordon equation

A three-time level finite-difference scheme based on a fourth order in time and second order in space approximation has been proposed for the numerical solution of the nonlinear two-dimensional sine-Gordon equation. The method, which is analysed for local truncation error and stability, leads to the solution of a nonlinear system. To avoid solving it, a predictor–corrector scheme using as predictor a second-order explicit scheme is proposed. The procedure of the corrector has been modified by considering as known the already evaluated corrected values instead of the predictor ones. This modified scheme has been tested on the line and circular ring soliton and the numerical experiments have proved that there is an improvement in the accuracy over the standard predictor–corrector implementation. This research was co-funded by E.U. (75%) and by the Greek Government (25%).