计算结果表明，其预报精度优于常规的统计回归模型。
The result shows that the prediction is more precise than that of the regression model.
人工神经网络不具备统计回归模型的显式表达形式，是一种隐式模型，因而有它的缺陷性。
Artificial neural network is a kind of implicit expression without explicit expression as statistical regression model, and it is its disadvantage.
实践表明：两种模型对毛条的豪特长度、长度变异、短纤维含量和精梳落毛率均能进行较为准确的预测，其中人工神经网络预测模型预测效果优于统计回归模型。
Application experience has been shown that both methods have good prediction performance in which the ANN method has better prediction performance in comparison with multiple regression method.
第三章是关于回归系数的可容许估计，这包括统计线性模型不受约束和受约束的情形。
In the third part, we give the admissible estimators of regression coefficients in statistical linear model with or without constraints.
运用回归技术和统计方法，建立了加速车道合流点分布概率的实测经验模型。
Then, the empirical distributing probability model of merging spots was set up by using regression techniques and statistical methods.
就这两类回归模型，从稳健统计的角度提出相应的稳健方法，并通过例子与现有的方法进行比较，说明所提方法的稳健性。
Based on robust statistics, robust approaches are represented for these fuzzy regression models, and are illustrated with several examples in order to show the robustness of the suggested approaches.
采用统计分析和偏最小二乘回归方法提出了过程稳定性在线评价模型，克服了输入变量严重多重相关性的问题。
Based on this, an on-line evaluation model of process stability with statistical method and partial-least-square regression (PLSR) was set up which overcome the multicollinearity of input parameters.
本文借鉴协整的思想，并采用比协整回归更一般化的方法来研究股票之间的统计套利模型。
We adopt the idea of cointegration and apply the methods that are more generalized than cointegration regression to study the Statistical Arbitrage Models of the securities.
提出了处理无耗正切关系网络测量数据的统计回归数学模型。
A statistical regressive mathematical model for processing the measured data of lossless network is presented.
研究了带有约束的均值漂移和方差加权的混合非线性回归模型．得到了相应的一阶和二阶诊断统计量。
We study restriction of Nonlinear Regression Diagnostic Models with case-weights and meanshifte simultanously, and some new diagnostic statistics are derved.
本文采用自回归模型得到肝脏组织不同区域的超声结构散射频谱，并对其频谱特征进行了统计分析。
In this paper, the ultrasonic spectra of liver's structural scattering were obtained by using autoregressive model, and the spectral characteristics were analyzed statistically.
研究序约束条件下自回归条件异方差（ARCH）模型的统计推断。
This paper deals with the statistical inference of an autoregressive conditional heteroscedasticity (ARCH) model under restriction.
对这类模型的统计建模，人们既关心回归系数的估计，更关心误差条件方差结构中未知参数的估计。
For this kind of modeling, people care for not only the estimation of regressive coefficient but also the estimation of unknown parameters in conditional skedasticity.
本文比较系统地研究了非线性分位点回归模型的统计诊断，尤其是影响分析等。
This thesis is devoted to investigate the statistical diagnostics, particularly the influence analysis, for nonlinear quantile regression.
侧重分析静态回归直线误差模型及工程应用的可行性，通过例证比较说明统计估算法的优势。
Based on the fact that total error is needed by the system, this thesis emphasizes particularly on analyzing the static regression line error model and its engineering feasibility.
根据野外观测资料及气象站观测的气象因子，利用多元统计方法，建立了各可燃物类型地表可燃物含水率与其相关的气象因子之间的回归模型。
According to the measurement of dead fuel moistures and meterological factors in field, by the means of multi-factors statistics, the models of dead fuel moisture were developed.
结果表明，所提出的的截面弯矩-曲率关系三折线模型中，各无量纲特征参数的统计回归公式具有较好的精度。
The results show that the regression formulas for the characteristic parameters in the proposed trilinear model are of excellent precision.
对于广义回归模型，人们也总是假设数据具有名义离差，否则，统计推断更加困难。
For generalized linear and nonlinear models, it is still a conventional hypothesis that observed data should have nominal dispersion, otherwise statistical inference would be more difficult.
该文针对风速随机变化的特性，在风速统计特性研究的基础上，用自回归滑动平均(ARMA)方法建立了具有一定功率谱密度特性的风速模型。
This paper deals with stochastic characteristic of the wind speed, and gives an auto-regressive moving-average (ARMA) model for wind speed subjected to particular power spectral density.
通过设计对道路运输企业进行能耗统计评价的回归模型，并以Q企业为例对我国道路运输企业的能耗问题进行实证分析。
Through the design of it's energy statistical evaluation of regression model, it USES with Q enterprises as an example for the empirical analysis of energy issues.
通过多元统计的逐步回归建立了QSRR模型，其计算误差应接近实验误差。
Through stepwise of multiple regressions, QSRR model has been established. The calculating errors of the model are similar to the experimental ones.
最后，在S-PLUS中进行以上两种回归分析计算，并进行了比较，给出复印机维修信息系统统计解析的结果，得到了数学模型的解。
Finally, I proceed regression analysis with such two method in the S-PLUS, gain the analysis result from the Duplicator Maintaining Information System, and get the solution of the mathematics model.
线性模型是很重要的一类统计模型，它包括线性回归模型、方差分析模型、协方差分析模型和方差分量模型等等。
Linear models are especially important statistical models, including linear regression model, variance and analysis, covariance and analysis, and variance and component one etc.
该算法由于不需要先验地建立一个参数未知的回归模型，因此可以用在其他传统统计回归算法失效的场合。
Since the algorithm does not require a prior regression model with unknown variables, it can be utilized in the circumstances where other traditional statistical regression algorithms fail.
线性模型是一类很重要的统计模型，它包括回归模型、方差分析模型、协方差分析模型以及混合模型等。
Linear model is a vital class of statistical model which involves regression model, variance analysis model, covariance analysis model and mixed model etc.
为此，结合统计学习理论的研究成果，建立了基于最小一乘准则的最优回归模型，并将其应用于商业银行的信贷风险评估中。
Thus, combined with research results of statistic learning theory, the optimal regress model based on least-absolute criteria, or LaOR model was proposed to solve the problem.
在此基础上设计储层保护数据库及敏感预测系统软件，并通过多元统计回归分析提出了储层敏感性预测模型。
Based on it, this article puts forward a reservation - layer protection data and sensitivity prediction system software, and raises a sensitivity prediction model through multiple - statistics.
其次，在此评价指标体系的基础上，利用统计中的线性回归模型的方法建立了评价模型。
Secondly, based on the system, evaluation model has set up by utilizing statistical linear regression model method.
对统计算法中回归模型中的假设条件、平滑指数的自适应调整、ARMA模型的参数估计作了一些分析。
We make analyses of the three hypotheses of regression. Adaptive adjustment of smoothing index parameters and parameter estimation of ARMA models.
对统计算法中回归模型中的假设条件、平滑指数的自适应调整、ARMA模型的参数估计作了一些分析。
We make analyses of the three hypotheses of regression. Adaptive adjustment of smoothing index parameters and parameter estimation of ARMA models.
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