线性回归法在时序数据聚集中的应用

文章基于线性回归法,提出了一种对电力负荷预测系统中的时序数据进行聚集的有效计算方案,并给出了实验和结果分析。     

线性回归在航材订货预测中的应用

线性回归法是一项重要的预测技术,它在相关分析中有着重要的应用。航材的订货数量如果能应用该技术进行预测就可以避免大量的浪费,从而既能保证正常的飞行训练,又能节约大量的经费。     

线性回归在分析化学中的应用

本文论述了线性回归方程的建立方法,以及如何用相关系数对其进行显著性检验,归纳了线性回归在分析化学中的应用,并介绍了利用计算机技术进行分析化学中回归分析的优点及方法。     

回归分析在企业效益中的应用

回归分析是数理统计中的一个重要内容,是利用统计学原理寻求隐藏在随机现象中的统计规律的计算方法和理论,它在各个学科领域以及社会经济各部门都得到广泛应用。运用回归分析建立回归模型,并通过逐步回归求得"最优"结果,利用最优回归模型对规模以上企业效益未来发展进行预测,从而为有关部门的决策提供一定的科学依据。     

回归分析法在物流成本预测中的应用

一、回归分析及一元线性回归法(一)回归分析概述回归分析是研究变量之间的依赖关系的一种数学方法。一般来说,变量之间的关系可大致分为两类:第一类是变量之间的关系完全确定,一个变量能够被一个或若干个其     

回归分析法在物流成本预测中的应用

一、回归分析及一元线性回归法 （一）回归分析概述回归分析是研究变量之间的依赖关系的一种数学方法。一般来说,变量之间的关系可大致分为两类：第一类是变量之间的关系完全确定,一个变量能够被一个或若干个其他变量按某一规律惟一确定,这种关系称为函数关系;第二类是变量之间具有非确定性的依赖关系,即变量之间既存在密切的数量关系,又不能由一个或几个变量精确求出另一个变量值,     

回归分析在城镇居民生活消费中的应用

本文根据1992年～2009年河南省城镇居民非商品支出与文化生活服务支出的基本数据,应用线性回归分析的方法研究了城镇居民非商品支出与文化生活服务支出之间数量关系的基本规律。进一步介绍了线性回归分析方法,建立了回归方程和进行相关性检验。帮助有关部门和经营者制订经济政策进而实施宏观调控等,对刺激经济持续、健康发展具有重要意义。     

线性回归分析法在新疆煤炭煤电煤化工人才需求预测中的应用

结合新疆煤炭煤电煤化工行业人力资源状况,分析"新型工业化"和"大企业大集团"生产技术条件下影响人才需求的因素,采用多元线性回归模型对新疆煤炭煤电煤化工行业未来人才需求进行了预测,提出了人才培养规划目标和人才培养措施,取得了较好效果。     

多元线性回归与ARIMA在中国人口预测中的比较研究

参考中国统计年鉴1970-2005年的数据,文章建立了多元线性回归模型和基于ARIMA算法的时间序列模型对我国人口进行预测,将结果与实际值进行比较,得出多元线性回归模型在人口预测上具有更高的精准度。两个模型同时表明,我国人口在短期内会继续增长,并且多元线性回归模型表明增长趋势会逐渐变缓。     

负荷预测模型的建立及基于回归分析法的负荷预测

文章介绍了几种不同负荷特性的定义及预测模型。根据负荷预测的基本步骤,结合某地区电网历史数据实际情况分析研究,限于同一季节中,温差变化不大时,在超短期预测中选择出一种一元线性预测回归模型。应用于算例分析,最终得到预测结果,精度较高,说明了该方法的实用性和有效性。     

Estimation and testing in time-series regression models with heteroscedastic disturbances

We consider ARMAX models with heteroscedastic residuals. Consistent estimation of the regression coefficient allows the Bicker-White approach to heteroscedasticity to be extended to moving averages of heteroscedastic disturbances. Tests for the presence of a moving-average or of heteroscedasticity are developed and estimation of the moving-average parameters considered.     

Testing fuzzy linear hypotheses in linear regression models

In this paper statistical tests with fuzzily formulated hypotheses are discussed, i.e., hypotheses H₀ and H₁ are fuzzy sets. The classical criteria of the errors of type I and type II are generalized, and this approach is applied to the linear hypothesis in the linear regression model. A sufficient condition to control both generalized criteria simultaneously is presented even in case of testing H₀ against the omnibus alternative H₁:¬ H₀. This is completely different from the classical case of testing crisp complementary hypotheses.     

Functional partially linear quantile regression model

This paper considers estimation of a functional partially quantile regression model whose parameters include the infinite dimensional function as well as the slope parameters. We show asymptotical normality of the estimator of the finite dimensional parameter, and derive the rate of convergence of the estimator of the infinite dimensional slope function. In addition, we show the rate of the mean squared prediction error for the proposed estimator. A simulation study is provided to illustrate the numerical performance of the resulting estimators.     

Estimating economic relations from incomplete cross-section/time-series data

Authors dealing with combined cross-section/time-series data usually assume that complete time-series exist for all units under observation. In the context of micro data, however, this may be a very restrictive assumption. The paper is concerned with problems of model specification and estimation when the data at hand are incomplete time-series from a sample of micro units. Particular attention is paid to a situation where the sample of micro units ‘rotates’ over time. The main results are compared with those derived by Nerlove and others for the standard specification with complete cross-section/time-series data. Some illustrative examples based on data from Norwegian household budget surveys are also given.     

Contamination in linear regression models and its influence on estimators

The consequences of the omission of possibly contaminated observations in a linear regression model for the performance of the ordinary least squares ( LS- ) estimator are discussed. We compare the ordinary L Sestimator with the corresponding 'never pooled' LS -estimator with respect to the matrix-valued mean squared error. Necessary and sufficient conditions are derived for the superiority of an estimator to another one and tests are proposed to check these conditions. Finally the resulting preliminary-test-estimators are investigated.     

Empirical likelihood for high-dimensional linear regression models

High-dimensional data are becoming prevalent, and many new methodologies and accompanying theories for high-dimensional data analysis have emerged in response. Empirical likelihood, as a classical nonparametric method of statistical inference, has proved to possess many good features. In this paper, our focus is to investigate the asymptotic behavior of empirical likelihood for regression coefficients in high-dimensional linear models. We give regularity conditions under which the standard normal calibration of empirical likelihood is valid in high dimensions. Both random and fixed designs are considered. Simulation studies are conducted to check the finite sample performance.     

Linear programming-based estimators in simple linear regression

In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the LPE. In the first case, the regressor is assumed to be fixed in repeated samples. In the second, the regressor is stochastic and potentially endogenous. For both cases the strong consistency and exact finite-sample distribution of the LPE is established. Conditions under which the LPE is consistent in the presence of serially correlated, heteroskedastic errors are also given. Finally, we describe how the LPE can be extended to the case with multiple regressors and conjecture that the extended estimator is consistent under conditions analogous to the ones given herein. Finite-sample properties of the LPE and extended LPE in comparison to the LSE and instrumental variable estimator (IVE) are investigated in a simulation study. One advantage of the LPE is that it does not require an instrument.     

Combining information for prediction in linear regression

This article deals with the prediction problem in linear regression where the measurements are obtained using k different devices or collected from k different independent sources. For the case of k=2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷ _c and ŷ _p are proposed. ŷ _c is based on a combined estimator of the regression coefficient vector, and ŷ _p is obtained by combining the individual predictors from different models. Prediction mean square errors of both predictors are derived. It is shown that the predictor ŷ _p is better than the individual predictors for k≥2 and the predictor ŷ _c is better than the individual predictors for k=2. Numerical comparison between ŷ _c and ŷ _p shows that the former is superior to the latter for the case k=2.     

Rank tests for testing randomness of a regression coefficient in a linear regression model

We propose a class of nonparametric tests for testing non-stochasticity of the regression parameterβ in the regression modely _i =βx _i +ɛ _i ,i=1, ...,n. We prove that the test statistics are asymptotically normally distributed both underH ₀ and under contiguous alternatives. The asymptotic relative efficiencies (in the Pitman sense) with respect to the best parametric test have also been computed and they are quite high. Some simulation studies are carried out to illustrate the results. Research was supported by the University Grants Commission, India.