Tractors in Spain: a Further Logistic Analysis

The three parameter logistic growth function is known to give a good fit to annual statistics of the number of tractors in Spain. A parameterisation directly yielding the upper asymptote or saturation level is advanced, and the variance-covariance matrix of the parameter estimates is computed. A generalisation of the logistic, which proved useful in studying the growth of the number of tractors in Great Britain is examined and gives appreciably different results.     

Tractors in Spain — A Dynamic Reanalysis

This paper presents a class of models obtained from the logistic curve to forecast stocks of goods. The dynamic approach allows variation of the parameters with time, thus allowing the model to adapt for changes. The observations are kept at the original scale and the model is reformulated in terms of the parameters. On-line estimates, forecasts and measures of performance are obtained. The methodology is applied to the stock of tractors in Spain. Fitted figures are obtained and compared with those from previous work.     

关于随机化Logistic模型的应用研究

以随机化的Logistic模型的差分形式为例,考察种群在异于通常意义下稳定性的某些性态,并力图更为客观地反映和刻画出生物现象及特性.所用数学工具较简单,易于在计算机上模拟.     

Logistic回归模型的影响分析

Logistic回归模型的影响分析是Logistic回归诊断研究中的重要内容。常用的分析方法都是轮换地删除数据点后的逐步判断,而这个判断的过程主要体现在模型的诊断图上。鉴于此,通过构造诊断统计量来有效地开发诊断图成为影响分析的核心内容,并由此能较为准确地探寻出模型的强影响点。本文通过对Logistic回归模型帽子矩阵的分解以及对轮换地删除数据点后的系数估计的相对变化量进行加权,得出Logistic回归模型诊断图使其能比传统的诊断图更准确地判断出模型的强影响点。     

Logistic回归模型在信用风险分析中的应用

通过运行SPSS,建立L og istic回归信用评价模型(cred it eva luation m odel),用来对中国2000年106家上市公司进行两类模式分类,这两类模式是指按照公司的经营状况分为“差”和“正常”两个小组.对每一家上市公司,考虑其经营状况的4个主要财务指标:每股收益、每股净资产、净资产收益率和每股现金流量.仿真结果表明,L og istic回归信用评价模型对总体106个样本,判别准确率达到99.06%.此外,本文的研究结果还发现,当利用SPSS的D iscrim inan t给出的模型系数建立的线性判别分析模型和利用SPSS的M u ltinom ia lL og istic给出的模型参数建立的L og istic回归模型,L og istic回归模型的判别结果不如线性判别模型.但如果剔除不合格的样本,或是将样本数据规格化,则可以提高L og istic回归模型的分类准确率.     

有Logistic增长的媒介传播模型分析

建立了贮存宿主鸟类与传染宿主蚊子都具有Logistic增长的西尼罗河病毒传播模型,获得了基本再生数R_0.当R_0<1时,通过构造Lyapunov函数,证明了无病平衡点的全局渐近稳定性.当R_0>1,且满足不同条件时,得到了正平衡点的存在性,数值模拟验证了理论结果的正确性.     

A Hybrid Logistic Model for Case-Control Studies

For logistic regression in case-control studies, when risk factors associated with the outcome are exceedingly rare in the control group, the estimation of parameters in the model becomes difficult. In this paper, we propose a two-stage hybrid method to achieve this. In the first stage, we model the risk due to the rare factor, and in the second stage we model the residual risk due to the other factors using standard logistic model.     

一类具有变时滞的随机logistic种群系统

主要是讨论了一类具有变时滞的随机logi8tic种群系统.首先探讨了系统全局正解的存在性;然后获得了系统弱持久性和灭绝性的充分条件,获得了种群系统弱持续生存与灭绝之间的临界值.     

判别分析与Logistic回归组合分类

本文讨论判别分析与Logistic回归组合方法的二分类问题.模拟结果显示,基于判别分析的Logistic回归的第一组合法的回判正确率往往高于判别分析分类和Logistic回归分类.第二组合法是基于Logistic回归的判别分析分类,回判正确率接近于Logistic回归的分类结果.很多情况下,这两种方法的回判正确率都高于判别分析。     

扬州市肺癌发病风险因素的Logistic回归分析

取扬州市中医院2010年1月-2013年12月住院治疗的肺癌患者,共425例,对照组为同期来院体检或探视病人的家属,共425例,采用自行设计的问卷进行调查,并使用Logistic回归分析来进行统计,分析扬州市肺癌病人发病的风险因素。结果表明,肺部疾病对肺癌发病率的影响最大,然后依次是内向忧郁的不良情绪,吸烟因素,患有高血压疾病;经常食用新鲜蔬菜瓜果则是肺癌的保护性因素.给出建议:应培养乐观积极的心态,大力提倡健康的生活方式,多吃新鲜蔬果,适当进行一些体育锻炼,加强控烟力度,防治慢性病.     

高血压性脑出血再发生因素的Logistic分析

根据问题的特点 ,选择 Logistic判别模型 ,并借鉴普通线性回归的逐步向前法 ,用似然比统计量 ( Deviance)来筛选自变量 (因素 ) .结果显示 :在 1 1个因素中 ,有 3个是作用显著的 ,而其中舒张压值极为重要 .为降低再出血率 ,将其控制在 85 mm Hg以下是有效而可行的     

基于Logistic回归的主成分估计及实证分析

应用主成分估计方法,对Logistic回归模型进行参数估计,并消除多重共线性影响.首先选取了累计贡献率达到85%以上的6个主成分,对因变量进行主成分估计,然后挑选出冠心病患者发病的主要影响因素,最后得到了因变量(冠心病发病)与6个主要影响因素(血压(sbp)、累计烟草量(tobacco)、低密度脂蛋白胆固醇(ldl)、心脏病家族史(famhist)、型表现(typea)和发病年龄(age))的回归模型.根据结果可知,心脏病家族史是导致心脏病发病最大的一个原因,它是一个不可控因素;在可控因素中,累计烟草量对冠心病发病的影响最大,因此建议患者应该控制烟草摄入量,以保证病情的稳定性.     

Logistic方程的紊动现象

对于单参数二维映射ｆ（ｘ，ｙ）＝（ｙ，μｙ（１－ｘ）），本文给出使ｆ存在Ｓｍａｌｅ马蹄的两个参数区间，从而对Ｌｏｇｉｓｔｉｃ方程的一些数值计算结果作出理论上的解释。     

A Bayesian Model to Predict Saturation and Logistic Growth

This paper formulates a Bayesian model to predict growth and eventual market saturation of a recently introduced (and possibly expensive) consumer-durable product. The mathematical model assumes that each new buyer buys only one item of the product, that the number of new buyers of the product in the next period of time is influenced by the current number of non-buyers and that the probability an individual will buy is the result of a diffusion of news among satisfied buyers. The solution of the prediction problem includes a two-stage Bayesian updating formula which first revises the prior distribution of market saturation based on the most recent number of new buyers and then, conditional on the saturation level, computes the predictive distribution of new buyers in future time-periods.