【摘要】：綜合評(píng)價(jià)的思想已經(jīng)深入到社會(huì)經(jīng)濟(jì)生活的各個(gè)方面,作為經(jīng)濟(jì)統(tǒng)計(jì)學(xué)的一個(gè)重要分支的統(tǒng)計(jì)綜合評(píng)價(jià)越來(lái)越引起社會(huì)的關(guān)注,已成為各類考核、評(píng)比、鑒定識(shí)別等活動(dòng)中行之有效且不失通俗性的工具。綜合評(píng)價(jià)的主要特征是定量與定性分析相結(jié)合,它是在定性分析的前提下,通過(guò)現(xiàn)象的數(shù)量表現(xiàn),對(duì)研究對(duì)象進(jìn)行更深刻的、更全面的認(rèn)識(shí)。就統(tǒng)計(jì)活動(dòng)過(guò)程而言,統(tǒng)計(jì)綜合評(píng)價(jià)是在統(tǒng)計(jì)調(diào)查、統(tǒng)計(jì)整理之后的一項(xiàng)重要工作,是發(fā)揮統(tǒng)計(jì)功能的重要環(huán)節(jié)。 在傳統(tǒng)的綜合評(píng)價(jià)中,數(shù)據(jù)格式都是以點(diǎn)值的形式來(lái)表現(xiàn)的。但由于評(píng)價(jià)方法的特性,不同的方法對(duì)數(shù)據(jù)結(jié)構(gòu)、評(píng)價(jià)模型均有不同的要求和規(guī)定,并且在綜合評(píng)價(jià)中,經(jīng)常會(huì)遇到數(shù)據(jù)模糊,數(shù)據(jù)不確定,數(shù)據(jù)獲得是范圍的情況,于是數(shù)據(jù)是區(qū)間形式的綜合評(píng)價(jià)就出現(xiàn)了。如何針對(duì)這種情況開(kāi)展評(píng)價(jià)活動(dòng),便成了我們所要研究的問(wèn)題之一。 然而綜合評(píng)價(jià)中的區(qū)間數(shù)據(jù)使用也存在著一系列的問(wèn)題,整體化思路下的區(qū)間評(píng)價(jià),其往往由于區(qū)間數(shù)的特性而要求發(fā)展出一種新的評(píng)價(jià)技術(shù),于是乎放棄了傳統(tǒng)評(píng)價(jià)技術(shù)的優(yōu)勢(shì)。筆者認(rèn)為在綜合評(píng)價(jià)過(guò)程中,區(qū)間數(shù)的“轉(zhuǎn)化”思路更符合實(shí)際操作,且“轉(zhuǎn)化”后的數(shù)據(jù)結(jié)構(gòu)能夠使用傳統(tǒng)的評(píng)價(jià)方法,使得評(píng)價(jià)更簡(jiǎn)便。故,相應(yīng)的整個(gè)區(qū)間數(shù)評(píng)價(jià)的問(wèn)題比較側(cè)重的轉(zhuǎn)移到了區(qū)間數(shù)的點(diǎn)值化問(wèn)題上,就如何合理進(jìn)行區(qū)間指標(biāo)點(diǎn)值化,本文提出了考慮類似于物理質(zhì)心確定的一種點(diǎn)值化方法。 本文的寫作思路是,將區(qū)間數(shù)點(diǎn)值化處理的前提條件分為兩種：分布信息已知和分布信息未知,再分別對(duì)兩種情況進(jìn)行處理。在分布信息已知條件下,筆者引入了物理質(zhì)心思想對(duì)區(qū)間信息進(jìn)行點(diǎn)值化處理；在分布信息未知條件下,筆者提出了“同指標(biāo)分布相似性假設(shè)”,并按照分布信息可能的形態(tài)來(lái)首先進(jìn)行分布估計(jì),從而轉(zhuǎn)化為分布信息已知完成點(diǎn)值化。 各章節(jié)的安排如下： 第一章,主要闡述了區(qū)間綜合評(píng)價(jià)技術(shù)的基本問(wèn)題。介紹了區(qū)間數(shù)評(píng)價(jià)中提出了區(qū)間數(shù)的產(chǎn)生,區(qū)間數(shù)的類型以及區(qū)間數(shù)基本的處理思路,探討了點(diǎn)值化作為綜合評(píng)價(jià)區(qū)間指標(biāo)處理的可行性,為整片文章打下基礎(chǔ)。 第二章,介紹了區(qū)間型符號(hào)數(shù)據(jù)運(yùn)算,重點(diǎn)研究區(qū)間型符號(hào)變量的統(tǒng)計(jì)描述,包括區(qū)間數(shù)的經(jīng)驗(yàn)密度函數(shù)計(jì)算、均值和方差、協(xié)方差和相關(guān)系數(shù)計(jì)算等,并針對(duì)綜合評(píng)價(jià)系統(tǒng)對(duì)區(qū)間數(shù)的指標(biāo)量化、指標(biāo)標(biāo)準(zhǔn)化進(jìn)行研究。 第三章,在理想情況區(qū)間輔助分布信息完全已知的條件下,首先假定變量分布情況是隨意的,即不同評(píng)價(jià)單元在相同變量指標(biāo)上的分布可以不同,同一評(píng)價(jià)單元的不同變量區(qū)間上分布也可以不同,且分布形式可以有偏也可以有峰,包括多峰情況。借鑒物理中利用質(zhì)點(diǎn)、質(zhì)量合成對(duì)不規(guī)則物體的質(zhì)點(diǎn)求解方法,提出了尋找評(píng)價(jià)單元信息空間上信息聚集點(diǎn)——質(zhì)點(diǎn),并利用累積分布計(jì)算質(zhì)點(diǎn)的評(píng)價(jià)信息含有量——質(zhì)量,通過(guò)質(zhì)點(diǎn)、質(zhì)量合成的區(qū)間信息點(diǎn)值化方法。 第四章,假設(shè)輔助信息未知,但同一指標(biāo)所具有的分布信息應(yīng)該類似,重點(diǎn)討論了分布信息是單峰情況的點(diǎn)值化處理(單峰情況的區(qū)間分布信息在實(shí)際中較為多見(jiàn),故單獨(dú)成章研究)。引入了計(jì)量學(xué)中的相空間重構(gòu)理論構(gòu)造單個(gè)指標(biāo)的區(qū)間數(shù)矩陣形式得到更大信息量,由于單峰狀態(tài)下可能存在偏峰,于是使用β分布對(duì)單個(gè)指標(biāo)的區(qū)間分布信息進(jìn)行擬合,通過(guò)參數(shù)估計(jì)求得分布函數(shù),并進(jìn)行點(diǎn)值化。 第五章,考慮區(qū)間型指標(biāo)內(nèi)部的有多峰存在,根據(jù)多峰分布的特點(diǎn),提出了兩種可行的處理方法：1、直接進(jìn)行多峰分布估計(jì)；2、分離峰后做單峰估計(jì)。前者舉例了一種可有雙峰形狀的概率密度函數(shù)來(lái)進(jìn)行說(shuō)明研究,后者則借鑒聚類判別的思想來(lái)進(jìn)行分離,并通過(guò)模擬算例來(lái)比較兩種方法。 第六章,總結(jié)與展望。對(duì)全文的研究?jī)?nèi)容進(jìn)行了總結(jié),對(duì)論文所存在的問(wèn)題以及需要進(jìn)一步深入研究的問(wèn)題進(jìn)行了闡述
[Abstract]:As an important branch of economic statistics, the comprehensive evaluation of statistics has attracted more and more social attention. It has become an effective and popular tool in all kinds of assessment, evaluation, identification and other activities. On the premise of qualitative analysis, it makes a deeper and more comprehensive understanding of the research object through the quantitative expression of phenomena. As far as the process of statistical activities is concerned, statistical comprehensive evaluation is an important work after statistical investigation and statistical collation, and it is an important link to exert statistical functions.
In the traditional comprehensive evaluation, the data format is expressed in the form of point value. However, because of the characteristics of the evaluation methods, different methods have different requirements and regulations for the data structure and evaluation model. In the comprehensive evaluation, the data are often fuzzy, uncertain, and the data is obtained in the scope of the situation, so the data is The interval form of comprehensive evaluation appears. How to carry out evaluation activities in view of this situation has become one of the problems we want to study.
However, there are a series of problems in the use of interval data in the comprehensive evaluation. The interval evaluation under the holistic thinking often requires the development of a new evaluation technology because of the characteristics of interval numbers, thus abandoning the advantages of traditional evaluation technology. Therefore, the problem of the evaluation of the whole interval number is transferred to the problem of the point value of the interval number. In this paper, we propose to consider the point value of the interval index, which is similar to the physical center of mass. A fixed point method.
The idea of this paper is to divide the preconditions of interval number point-valued processing into two kinds: the distribution information is known and the distribution information is unknown, and then deal with them separately. The distribution similarity hypothesis of the same index is put forward, and the distribution is estimated according to the possible form of the distribution information, and then the distribution information is converted into the known distribution information.
The chapters are arranged as follows:
The first chapter mainly expounds the basic problems of interval comprehensive evaluation technology, introduces the generation of interval numbers, the types of interval numbers and the basic processing ideas of interval numbers in interval number evaluation, discusses the feasibility of point-valued as an interval index of comprehensive evaluation, and lays a foundation for the whole article.
In the second chapter, the operation of interval symbolic data is introduced, and the statistical description of interval symbolic variables is mainly studied, including the calculation of empirical density function, mean and variance, covariance and correlation coefficient of interval numbers.
In Chapter 3, under the condition that the information of interval auxiliary distribution is completely known, the distribution of variables is assumed to be random, that is, the distribution of different evaluation units on the same variable index can be different, the distribution of different variables on the same evaluation unit can be different, and the distribution form can be biased or peaked, including Referring to the method of Solving Irregular object's particle by using particle and mass synthesis in physics, this paper puts forward a method of finding information aggregation point-particle in evaluation unit's information space, and calculates the quantity-quality of evaluation information by using cumulative distribution.
In Chapter 4, assuming that the auxiliary information is unknown, but the distribution information of the same index should be similar, the point-valued processing of the distribution information in the case of single peak is discussed emphatically. Interval matrix can get more information, because there may be biased peaks in the single peak state, so the beta distribution is used to fit the information of the interval distribution of a single index, and the distribution function is obtained by parameter estimation.
In the fifth chapter, considering the existence of multi-peaks within the interval index, two feasible methods are proposed according to the characteristics of multi-peaks distribution: 1. directly estimating multi-peaks distribution; 2. estimating single-peaks after separating peaks. The former illustrates a probability density function with bimodal shape, and the latter uses clustering discrimination for reference. To separate the ideas, and compare the two methods through simulation examples.
Chapter 6, Summarization and Prospect. Summarize the research content of the full text, expound the problems existing in the paper and the problems needing further study.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：C81

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳又星;;基于區(qū)間數(shù)灰理論的短期經(jīng)濟(jì)增長(zhǎng)波動(dòng)研究[J];重慶工商大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2011年02期

2 蔣仁言;一種可有雙峰形狀的概率密度函數(shù)[J];長(zhǎng)沙交通學(xué)院學(xué)報(bào);1997年01期

3 尤天慧,樊治平;區(qū)間數(shù)多指標(biāo)決策的一種TOPSIS方法[J];東北大學(xué)學(xué)報(bào);2002年09期

4 于春海,樊治平;一種基于風(fēng)險(xiǎn)態(tài)度因子的區(qū)間數(shù)信息聚類方法[J];東北大學(xué)學(xué)報(bào);2003年11期

5 樊治平,宮賢斌,張全;區(qū)間數(shù)多屬性決策中決策矩陣的規(guī)范化方法[J];東北大學(xué)學(xué)報(bào);1999年03期

6 徐澤水,達(dá)慶利;區(qū)間型多屬性決策的一種新方法[J];東南大學(xué)學(xué)報(bào)(自然科學(xué)版);2003年04期

7 孟廣武,張興芳,鄭亞林;基于區(qū)間值模糊集的聚類方法[J];工程數(shù)學(xué)學(xué)報(bào);2001年02期

8 徐改麗;呂躍進(jìn);;基于正態(tài)分布區(qū)間數(shù)的多屬性決策方法[J];系統(tǒng)工程;2011年09期

9 黃德鏞,胡運(yùn)權(quán),戴曉江;基于逆序概率的隨機(jī)模擬決策方法研究[J];管理工程學(xué)報(bào);2003年02期

10 李汶華;郭均鵬;高峰;;區(qū)間型符號(hào)數(shù)據(jù)的因子分析及其應(yīng)用[J];管理工程學(xué)報(bào);2009年04期

相關(guān)博士學(xué)位論文 前1條

1 陳驥;基于區(qū)間數(shù)的綜合評(píng)價(jià)問(wèn)題研究[D];浙江工商大學(xué);2010年

，

本文編號(hào)：2220332