湖北省工業(yè)企業(yè)創(chuàng)新投入產(chǎn)出的多元非線性回歸模型研究
發(fā)布時間：2019-06-06 04:47
【摘要】：創(chuàng)新能力是科技創(chuàng)新驅(qū)動發(fā)展戰(zhàn)略的根本,是國家競爭力的具體體現(xiàn),決定了國民經(jīng)濟(jì)的長期動力,預(yù)示著未來幾年的經(jīng)濟(jì)發(fā)展趨勢。因此,對創(chuàng)新能力的研究具有十分重要的意義。對于政府職能部門和企業(yè)這兩個可調(diào)控的主體,深入改革促進(jìn)創(chuàng)新的前提是能正確地評估企業(yè)的創(chuàng)新能力。在此背景下,2012年我們參與并完成了《湖北省工業(yè)企業(yè)創(chuàng)新能力綜合評價》項目,項目成果收錄在《湖北省第二次RD資源清查論文集》中。 投入產(chǎn)出模型是目前經(jīng)濟(jì)、管理學(xué)科研究中的重要工具,被廣泛應(yīng)用于各類綜合評價及預(yù)測中。實際的投入產(chǎn)出模型中,指標(biāo)體系往往比較龐大復(fù)雜,模型的建立大多基于多元線性回歸方法。但是多元線性模型的建立經(jīng)常會遇到兩類的問題,第一類是模型的選擇,另一類是數(shù)據(jù)的可靠性。比如：異方差問題,我們往往需要先對變量進(jìn)行轉(zhuǎn)換,這樣與之所匹配的模型就不再是多元線性模型；又或者數(shù)據(jù)存在異常值,多元線性模型會受異常值影響而產(chǎn)生較大偏差。在本文中,我們采取了多元非線性回歸及加權(quán)多元非線性回歸的方法解決上述問題,并得到了較為合理的結(jié)果,得到了相關(guān)專家的認(rèn)可。 本文選取湖北省工業(yè)企業(yè)作為研究對象,數(shù)據(jù)來源于2010-2012三年的《湖北省科技統(tǒng)計年鑒》,在構(gòu)建科學(xué)合理的創(chuàng)新能力評價指標(biāo)體系的基礎(chǔ)上,對湖北省工業(yè)企業(yè)的創(chuàng)新能力建立投入產(chǎn)出模型進(jìn)行研究。本文具體內(nèi)容安排如下： 第一章為緒論。論述了本文相關(guān)的研究背景、研究意義和研究現(xiàn)狀,敘述了本項目的主要研究內(nèi)容和方法以及研究重點與特色。 第二章為湖北省工業(yè)企業(yè)創(chuàng)新能力綜合評價基本情況介紹,包括創(chuàng)新能力綜合評價指標(biāo)體系的構(gòu)建和基本指標(biāo)的說明。本文建立的創(chuàng)新能力投入產(chǎn)出模型是綜合評價的重要組成部分,其投入產(chǎn)出指標(biāo)選取具備一定的科學(xué)性和合理性。 第三章為多元非線性回歸模型理論及其應(yīng)用。介紹了非線性回歸模型的一般理論方法。特別提出了加權(quán)多元非線性回歸方法,并舉例說明了該方法的可行性。 第四章為湖北省工業(yè)企業(yè)創(chuàng)新投入產(chǎn)出多元非線性回歸模型的實現(xiàn)。首先利用一般多元線性回歸的方法建立投入產(chǎn)出模型,針對某些指標(biāo)建立多元線性回歸模型的不合理情況,我們進(jìn)行了相應(yīng)的非線性化處理；其次根據(jù)殘差圖發(fā)現(xiàn)出現(xiàn)的異常值,采取人工剔除異常值,繼而使用“去異常值”的數(shù)據(jù)構(gòu)建投入產(chǎn)出模型；最后提出并實現(xiàn)了“利用殘差距離定義加權(quán)權(quán)重”的加權(quán)多元非線性回歸模型,一定程度上避免了“去異常值”過程中的主觀性和復(fù)雜性,結(jié)果也比較理想。 第五章為結(jié)論與展望。綜述了全文結(jié)論,并討論了本文的加權(quán)非線性回歸模型。實證分析加權(quán)非線性回歸模型具有一定可行性和合理性,但是其相關(guān)的理論性質(zhì)以及更優(yōu)的權(quán)重設(shè)置方法都值得深入研究。
[Abstract]:Innovation ability is the foundation of the strategy driven by scientific and technological innovation, and the concrete embodiment of national competitiveness, which determines the long-term driving force of the national economy and indicates the trend of economic development in the next few years. Therefore, the study of innovation ability is of great significance. For the government functional departments and enterprises, the premise of in-depth reform to promote innovation is to correctly evaluate the innovation ability of enterprises. In this context, in 2012, we participated in and completed the "Comprehensive Evaluation of Innovation ability of Industrial Enterprises in Hubei Province". The results of the project are included in the second RD Resource inventory of Hubei Province. Input-output model is an important tool in the research of economy and management, which is widely used in all kinds of comprehensive evaluation and prediction. In the actual input-output model, the index system is often large and complex, and most of the models are based on multiple linear regression methods. However, the establishment of multivariate linear models often encounters two kinds of problems, the first is the choice of models, the other is the reliability of data. For example, in the heteroscedasticity problem, we often need to transform the variables first, so that the matching model is no longer a multivariate linear model, or if there are outliers in the data, the multivariate linear model will be affected by the outliers and produce a large deviation. In this paper, we adopt the methods of multivariate nonlinear regression and weighted multivariate nonlinear regression to solve the above problems, and obtain more reasonable results, which have been recognized by relevant experts. In this paper, Hubei industrial enterprises are selected as the research object, and the data come from the Hubei Science and Technology Statistical Yearbook from 2010 to 20123. on the basis of constructing a scientific and reasonable evaluation index system of innovation ability, The input-output model of innovation ability of industrial enterprises in Hubei Province is studied. The specific content of this paper is arranged as follows: the first chapter is the introduction. This paper discusses the related research background, research significance and research status, and describes the main research contents and methods, as well as the research focus and characteristics of this project. The second chapter introduces the comprehensive evaluation of innovation ability of industrial enterprises in Hubei Province, including the construction of comprehensive evaluation index system of innovation ability and the explanation of basic indicators. The input-output model of innovation ability established in this paper is an important part of comprehensive evaluation, and its input-output index selection is scientific and reasonable. The third chapter is the theory of multivariate nonlinear regression model and its application. The general theory and method of nonlinear regression model are introduced. In particular, a weighted multivariate nonlinear regression method is proposed, and an example is given to illustrate the feasibility of the method. The fourth chapter is the realization of multiple nonlinear regression model of innovation input-output in Hubei Province. Firstly, the input-output model is established by using the general multivariate linear regression method, and the corresponding nonlinear treatment is carried out in view of the unreasonable situation of establishing the multivariate linear regression model for some indexes. Secondly, according to the abnormal values found by residual map, the abnormal values are manually eliminated, and then the input-output model is constructed by using the data of "removing outliers". Finally, a weighted multivariate nonlinear regression model of "using residual distance to define weighted weight" is proposed and implemented, which avoids the subjectivity and complexity in the process of "de-abnormal value" to a certain extent, and the results are also satisfactory. The fifth chapter is the conclusion and prospect. The conclusion of this paper is reviewed, and the weighted nonlinear regression model is discussed. Empirical analysis of weighted nonlinear regression model is feasible and reasonable, but its related theoretical properties and better weight setting methods are worthy of further study.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F425;F273.1;O212.1
[Abstract]:Innovation ability is the foundation of the strategy driven by scientific and technological innovation, and the concrete embodiment of national competitiveness, which determines the long-term driving force of the national economy and indicates the trend of economic development in the next few years. Therefore, the study of innovation ability is of great significance. For the government functional departments and enterprises, the premise of in-depth reform to promote innovation is to correctly evaluate the innovation ability of enterprises. In this context, in 2012, we participated in and completed the "Comprehensive Evaluation of Innovation ability of Industrial Enterprises in Hubei Province". The results of the project are included in the second RD Resource inventory of Hubei Province. Input-output model is an important tool in the research of economy and management, which is widely used in all kinds of comprehensive evaluation and prediction. In the actual input-output model, the index system is often large and complex, and most of the models are based on multiple linear regression methods. However, the establishment of multivariate linear models often encounters two kinds of problems, the first is the choice of models, the other is the reliability of data. For example, in the heteroscedasticity problem, we often need to transform the variables first, so that the matching model is no longer a multivariate linear model, or if there are outliers in the data, the multivariate linear model will be affected by the outliers and produce a large deviation. In this paper, we adopt the methods of multivariate nonlinear regression and weighted multivariate nonlinear regression to solve the above problems, and obtain more reasonable results, which have been recognized by relevant experts. In this paper, Hubei industrial enterprises are selected as the research object, and the data come from the Hubei Science and Technology Statistical Yearbook from 2010 to 20123. on the basis of constructing a scientific and reasonable evaluation index system of innovation ability, The input-output model of innovation ability of industrial enterprises in Hubei Province is studied. The specific content of this paper is arranged as follows: the first chapter is the introduction. This paper discusses the related research background, research significance and research status, and describes the main research contents and methods, as well as the research focus and characteristics of this project. The second chapter introduces the comprehensive evaluation of innovation ability of industrial enterprises in Hubei Province, including the construction of comprehensive evaluation index system of innovation ability and the explanation of basic indicators. The input-output model of innovation ability established in this paper is an important part of comprehensive evaluation, and its input-output index selection is scientific and reasonable. The third chapter is the theory of multivariate nonlinear regression model and its application. The general theory and method of nonlinear regression model are introduced. In particular, a weighted multivariate nonlinear regression method is proposed, and an example is given to illustrate the feasibility of the method. The fourth chapter is the realization of multiple nonlinear regression model of innovation input-output in Hubei Province. Firstly, the input-output model is established by using the general multivariate linear regression method, and the corresponding nonlinear treatment is carried out in view of the unreasonable situation of establishing the multivariate linear regression model for some indexes. Secondly, according to the abnormal values found by residual map, the abnormal values are manually eliminated, and then the input-output model is constructed by using the data of "removing outliers". Finally, a weighted multivariate nonlinear regression model of "using residual distance to define weighted weight" is proposed and implemented, which avoids the subjectivity and complexity in the process of "de-abnormal value" to a certain extent, and the results are also satisfactory. The fifth chapter is the conclusion and prospect. The conclusion of this paper is reviewed, and the weighted nonlinear regression model is discussed. Empirical analysis of weighted nonlinear regression model is feasible and reasonable, but its related theoretical properties and better weight setting methods are worthy of further study.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F425;F273.1;O212.1
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