共查询到20条相似文献,搜索用时 109 毫秒
1.
质量调整的价格指数编制中hedonic插补法的应用 总被引:1,自引:0,他引:1
在数据缺失的情况下,插补法是一种常用的推断缺失数据的方法。在价格指数的编制中,在基期存在的产品可能在报告期从市面上消失,或者报告期出现了新产品。这都可以看作是数据缺失的情形。同时由于前后时期产品质量发生变化,所编制的价格指数中可能包含"质量变化偏差"。Hedonic插补法将hedonic方法与缺失数据的插补方法结合起来,既处理了缺失数据,又克服了价格指数中的质量变化偏差。本文讨论了hedonic插补法的多种可能形式,并比较了各种方法的特点。本文还利用中国笔记本电脑的数据编制了hedonic插补价格指数,进行了相关的实证分析。 相似文献
2.
基于主成分分析的成分数据缺失值插补法 总被引:1,自引:0,他引:1
《应用概率统计》2016,(1)
本文针对成分数据的特殊几何结构,提出了两种新方法对成分数据缺失值进行插补.一种是用单形空间的均值进行插补,主要是用Aitchison足巨离找到含缺失值样本的k个近邻样本,再结合单形空间中的加法运算与数乘运算,用单形空间上的均值对成分数据的缺失值进行插补;另一种是用主成分回归方法进行插补,先将用第一种方法进行初始插补的成分数据经过等距对数比变换变成普通数据,再用主成分回归进行第二次插补.实例分析和实验模拟结果表明:与k近邻插补法、迭代的最小二乘插补法相比较,本文提出的主成分插补法更优. 相似文献
3.
《数学的实践与认识》2013,(21)
在实际的调查数据和实验数据中,经常会出现数据缺失的问题,插补方法是处理缺失数据的一种常用的技术方法.对于目标变量是二分类的定性变量时,可以采用Logistic回归插补法进行插补,采用一套高中生进入大学学习影响因素分析的模拟数据进行实证分析,探讨了Logi8tic回归插补法的一些特点. 相似文献
4.
5.
在时间序列建模过程中,数据的缺失会极大地影响模型的准确性,因此对缺失数据的填补尤为重要.选取北京市空气质量指数(AQI)数据。将其随机缺失10%.分别利用EM算法和polyfit直线拟合的方法对缺失值插补,补全数据后建立ARMA模型并作预测分析.结果表明,利用polyfit函数插补法具有较好的结果. 相似文献
6.
<正>割补法就是通过对图形的分割或补形,将复杂图形简单化、非规则图形规则化,并解决问题的一种方法.在立体几何中,恰当地运用割补法解题,不仅有助于培养学生的空间想象能力,同时也有助于培养同学们的分析问题、解决问题的能力. 相似文献
7.
8.
对于空间几何体,一般情况下求体积都能直接应用体积公式来解决,但是对于一些特例问题则不能直接解决,下面介绍两种方法来解决与体积相关问题.1.用"割补法"解决不规则几何体的体积一般地说,对于不是常见的柱、锥、台、球,通常有两种方法,一是将其分割,把它分割成若干个能直接应用公式求体积的几何体,二是在原来的几何体的基础上补形,补成一个能直接应用公式求体积的几何体,不过此时要求所补部分的体积易求或能够用所求几何体的体积来表示,通常把上述方法称为"割补法". 相似文献
9.
10.
11.
New imputation methods for missing data using quantiles 总被引:1,自引:0,他引:1
J.F. Muñoz 《Journal of Computational and Applied Mathematics》2009,232(2):305-317
The problem of missing values commonly arises in data sets, and imputation is usually employed to compensate for non-response. We propose a novel imputation method based on quantiles, which can be implemented with or without the presence of auxiliary information. The proposed method is extended to unequal sampling designs and non-uniform response mechanisms. Iterative algorithms to compute the proposed imputation methods are presented. Monte Carlo simulations are conducted to assess the performance of the proposed imputation methods with respect to alternative imputation methods. Simulation results indicate that the proposed methods perform competitively in terms of relative bias and relative root mean square error. 相似文献
12.
Dealing with the missing values is an important object in
the field of data mining. Besides, the properties of compositional data lead to
that traditional imputation methods may get undesirable result if they are directly
used in this type of data. As a result, the management of missing values in
compositional data is of great significant. To solve this problem, this paper
uses the relationship between compositional data and Euclidean data, and proposes
a new method based on Random Forest for missing values in compositional data.
This method has been implemented and evaluated using both simulated and real-world
databases, then the experimental results reveal that the new imputation method
can be widely used in various types of data sets and has good performance than
other methods. 相似文献
13.
In this paper, we investigate the model checking problem for a partial linear model while some responses are missing at random. By imputation and marginal inverse probability weighted methods, two completed data sets are constructed. Based on the two completed data sets, we build two empirical process-based tests for examining the adequacy of partial linearity of the model. The asymptotic distributions of the test statistics under the null hypothesis and local alternative hypotheses are obtained respectively. A re-sampling approach is applied to obtain the approximation to the null distributions of the test statistics. Simulation results show that the proposed tests work well and both proposed methods have better finite sample properties compared with the complete case (CC) analysis which discards all the subjects with missing data. 相似文献
14.
复制数据是处理抽样调查中数据项目缺失的一种常用方法。在两种常见模型及复杂抽样设计下,本文对处理数据项目缺失的类均值复制和类加权均值复制方法进行了对比。 相似文献
15.
16.
Thomas Nittner 《Computational Statistics》2004,19(2):261-282
Summary The main purpose of this paper is a comparison of several imputation methods within the simple additive modelty =f(x) + ε where the independent variableX is affected by missing completely at random. Besides the well-known complete case analysis, mean imputation plus random noise,
single imputation and two kinds of nearest neighbor imputations are used. A short introduction to the model, the missing mechanism,
the inference, the imputation methods and their implementation is followed by the main focus—the simulation experiment. The
methods are compared within the experiment based on the sample mean squared error, estimated variances and estimated biases
off(x) at the knots. 相似文献
17.
18.
Ying Wang Weiming Wan Rui-Sheng Wang Enmin Feng 《Journal of Computational and Applied Mathematics》2009
Mutual information can be used as a measure for the association of a genetic marker or a combination of markers with the phenotype. In this paper, we study the imputation of missing genotype data. We first utilize joint mutual information to compute the dependence between SNP sites, then construct a mathematical model in order to find the two SNP sites having maximal dependence with missing SNP sites, and further study the properties of this model. Finally, an extension method to haplotype-based imputation is proposed to impute the missing values in genotype data. To verify our method, extensive experiments have been performed, and numerical results show that our method is superior to haplotype-based imputation methods. At the same time, numerical results also prove joint mutual information can better measure the dependence between SNP sites. According to experimental results, we also conclude that the dependence between the adjacent SNP sites is not necessarily strongest. 相似文献
19.
In this paper, considering of the special geometry of compositional
data, two new methods for estimating missing values in compositional data are introduced. The
first method uses the mean in the simplex space which mainly finds the-nearest neighbor
procedure based on the Aitchison distance, combining with two basic operations on the simplex,
perturbation and powering. As a second proposal the principal component regression imputation
method is introduced which initially starts from the result of the proposed the mean in the
simplex. The method uses ilr transformation to transform the compositional data set, and then
uses principal component regression in a transformed space. The proposed methods are tested
on real data and simulated data sets, the results show that the proposed methods work well. 相似文献
20.
Kernel function method has been successfully used for the
estimation of a variety of function. By using the kernel function theory, an imputation
method based on Epanechnikov kernel and its modification were proposed to solve the
problem that missing data in compositional caused the failures of existing statistical
methods and the k-nearest imputation didn't consider the different contributions of
the k nearest samples when it used them to estimated the missing data. The experimental
results illustrate that the modified imputation method based on Epanechnikov kernel
get a more accurate estimation than k-nearest imputation for compositional data. 相似文献