氟硅酸化学沉淀法净化萃余酸中的镁

通过二次回归正交设计,实验研究了化学沉淀法净化未经浓缩的革余酸中镁离子的工艺条件,对氟硅酸用量、萃余酸水去除量、反应温度和反应时间四个因子进行了研究。实验结果表明:当氟硅酸用量为95.71g,除水量为80.41g,反应温度为36.02℃,反应时间为45.03min时,可使镁离子去除率达到最佳值,达到生产优等DAP的磷酸要求;实验得到的镁去除率(y)与氟硅酸用量(z_1)、除水量(z_2)、反应温度(z_3)和反应时间(z_4)的模型预测方程为:y= 59.1785-0.541156z_1+0.425071z_2-0.660951z_3-1.635317z_1²+1.768750z_1z_2-6.392974z_22+1.768750z_1z_2-6.392974z_2²+1.011250z_1z_3-0.978750z_2z_3-0.611140z_32+1.011250z_1z_3-0.978750z_2z_3-0.611140z_3²-1.106885z_42-1.106885z_4².     

基于半参数回归模型的最小一乘局部线性算法

根据最小一乘准则,推导出最小一乘局部线性估计的计算方法,并通过对模拟数据的计算和分析,对比最小一乘核算法和最小二乘局部线性算法,验证了最小一乘局部线性算法是一种有效的,稳健的估计方法,并且有降低边界效应的作用．     

基于具有LR型模 糊输出回归模型的上证指数预测

以对称二次型模糊回归模型为基础,引入一般二次模糊回归模型,利用模糊最小二乘法估计未知参数．构建评价标准考察两模型的拟合效果,在样本期内和样本期外分别评价模型的实际拟合与预测能力．     

基于强混合数据的半参数平滑转换回归模型

本文基于严平稳强混合数据和带确定性趋势的强混合数据序列,推广了文献[20]中提出的半参数平滑转换回归模型。对含于平滑转换函数中的未知光滑有界函数应用级数估计方法,并基于非线性最小二乘估计和级数估计理论证明了模型参数估计量的相合性和渐近正态性等大样本性质,简要讨论了其协方差矩阵的估计以及假设检验问题。最后,应用该模型重新研究了我国年度通货膨胀率的平滑转换结构。     

跳汰机的性能及统计模型

在煤用重选设备评定的国际标准（ＩＳＯ９２３）和以之为蓝本的国家标准（ＧＢ／Ｔ１５７１５）中的第一项评定指标“可能偏差”及和其有关的“不完善度”的确定是由以下步骤决定：１．由仅仅一次浮沉试验取得一组数据（一个观察值）并由此计算两段分离所得产品的分配率;２．用手工凭想象中的“Ｓ型曲线”把分配率中的两组６ 至８ 个点联起来得到两条“分配曲线”;３．由这两条粗糙的曲线“量出”各自的２５％ 和７５％ 分位点,以此得到能反映重选设备分离能力好坏的两个指标：“可能偏差”Ｅ和“不完善度”Ｉ,很难想象,这样由一个观察值得到的指标并通过没有模型的手工绘图而得到的结果会有任何实际意义,本文建议利用尽可能多的观察值来拟合ｌｏｇｉｓｔｉｃ回归模型,并依此得到分配曲线和计算出参数Ｅ和Ｉ,本文还利用一组实际数据来说明我们方法的合理性     

利用回归分析对正交试验结果进行修正

本文以纱线上浆率工艺参数正交试验为实例 ,利用回归分析的方法加以验证和修正 ,使工艺参数更符合实际情况 ,说明两种方法有较好的互补性 .     

线性回归在林区生产决策中的应用

在黑龙江省伊春林区 ,以 16个林业局调查的数据为依据 ,并由木材年采伐量与剩余物密切相关这一特点 ,应用数理统计知识 ,建立了回归分析方程 ,从而对木材采伐量和剩余物做出预测和控制 ,为保护森林生态环境及林区工业生产的宏观决策 ,提供了重要的参考数据     

Pyramid Quantile Regression

We describe a Bayesian model for simultaneous linear quantile regression at several specified quantile levels. More specifically, we propose to model the conditional distributions by using random probability measures, known as quantile pyramids, introduced by Hjort and Walker. Unlike many existing approaches, this framework allows us to specify meaningful priors on the conditional distributions, while retaining the flexibility afforded by the nonparametric error distribution formulation. Simulation studies demonstrate the flexibility of the proposed approach in estimating diverse scenarios, generally outperforming other competitive methods. We also provide conditions for posterior consistency. The method is particularly promising for modeling the extremal quantiles. Applications to extreme value analysis and in higher dimensions are also explored through data examples. Supplemental material for this article is available online.     

Test-Based Variable Selection via Cross-Validation

Abstract

Test-based variable selection algorithms in regression often are based on sequential comparison of test statistics to cutoff values. A predetermined a level typically is used to determine the cutoffs based on an assumed probability distribution for the test statistic. For example, backward elimination or forward stepwise involve comparisons of test statistics to prespecified t or F cutoffs in Gaussian linear regression, while a likelihood ratio. Wald, or score statistic, is typically used with standard normal or chi square cutoffs in nonlinear settings. Although such algorithms enjoy widespread use, their statistical properties are not well understood, either theoretically or empirically. Two inherent problems with these methods are that (1) as in classical hypothesis testing, the value of α is arbitrary, while (2) unlike hypothesis testing, there is no simple analog of type I error rate corresponding to application of the entire algorithm to a data set. In this article we propose a new method, backward elimination via cross-validation (BECV), for test-based variable selection in regression. It is implemented by first finding the empirical p value α*, which minimizes a cross-validation estimate of squared prediction error, then selecting the model by running backward elimination on the entire data set using α* as the nominal p value for each test. We present results of an extensive computer simulation to evaluate BECV and compare its performance to standard backward elimination and forward stepwise selection.