混合Logit模型具有较高的灵活性,其效用变量的系数可以服从正态分布、对数正态分布、SB分布等多种形式.本文以北京市居民出行调查数据为基础,建立交通方式选择的混合Logit模型,允许系数取不同分布类型的组合,采用极大模拟似然法完成参...混合Logit模型具有较高的灵活性,其效用变量的系数可以服从正态分布、对数正态分布、SB分布等多种形式.本文以北京市居民出行调查数据为基础,建立交通方式选择的混合Logit模型,允许系数取不同分布类型的组合,采用极大模拟似然法完成参数估计.在模拟中使用拟随机数序列计算模拟概率,首先使用变序Halton序列给出几种高精度的结果,进一步采用MLHS(Modified Latin Hypercube Sampling)方法对其中最好的假设模拟求解,为效用变量的系数确定了合适的随机分布函数.参数的估计值清晰地解释了影响人们出行的各种因素.展开更多
The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain par...The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.展开更多
文摘混合Logit模型具有较高的灵活性,其效用变量的系数可以服从正态分布、对数正态分布、SB分布等多种形式.本文以北京市居民出行调查数据为基础,建立交通方式选择的混合Logit模型,允许系数取不同分布类型的组合,采用极大模拟似然法完成参数估计.在模拟中使用拟随机数序列计算模拟概率,首先使用变序Halton序列给出几种高精度的结果,进一步采用MLHS(Modified Latin Hypercube Sampling)方法对其中最好的假设模拟求解,为效用变量的系数确定了合适的随机分布函数.参数的估计值清晰地解释了影响人们出行的各种因素.
文摘The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.
