【摘要】：作為從十九世紀末就逐漸得到認可并延續(xù)至今的抽樣調(diào)查技術(shù),不僅僅在獲取數(shù)據(jù)和統(tǒng)計推斷方面發(fā)揮了作用,而且代表了一種在保證數(shù)據(jù)結(jié)構(gòu)不變的情況下將數(shù)據(jù)以大化小思想。但是,在大數(shù)據(jù)時代下,不斷改進的存儲設(shè)備和計算能力讓人們擁有了取之不盡、用之不竭的數(shù)據(jù)資源,還有更加接近結(jié)果的預(yù)測方法,人們似乎無需再設(shè)定總體,更不用再根據(jù)抽樣調(diào)查的設(shè)計方案在總體中抽取樣本,取而代之的是具有全數(shù)據(jù)形式的大數(shù)據(jù)。所以有越來越多的人開始忽略抽樣調(diào)查,認為大數(shù)據(jù)時代下抽樣調(diào)查已經(jīng)沒有存在的意義。大數(shù)據(jù)時代是不是就不需要抽樣了?大數(shù)據(jù)背景下抽樣面臨哪些挑戰(zhàn)?如何做好大數(shù)據(jù)時代的抽樣?本文的寫作是圍繞這樣一系列問題而進行的探索和思考,文中首先從抽樣調(diào)查在誕生的歷史背景出發(fā),指出抽樣調(diào)查是在實踐中為了用少量數(shù)據(jù)代表總體數(shù)據(jù)而產(chǎn)生的,并明確了抽樣的學(xué)科地位其發(fā)展過程中為調(diào)查工作所發(fā)揮的價值。然后,結(jié)合大數(shù)據(jù)時代的特點和抽樣調(diào)查自身的不足,說明了大數(shù)據(jù)時代下抽樣調(diào)查面臨的挑戰(zhàn),包括抽樣調(diào)查中的非抽樣誤差和抽樣誤差的存在,還有對子總體估計的困難性等。接著,為了闡述大數(shù)據(jù)時代下抽樣調(diào)查在仍然有其存在的必要性,對大數(shù)據(jù)、總體、大樣本和全數(shù)據(jù)等概念進行了辨析,從概念和定義上表明了大數(shù)據(jù)既不是抽樣調(diào)查中所說的總體,也不能代表全數(shù)據(jù);同時對大數(shù)據(jù)和抽樣數(shù)據(jù)的來源和特征以及在數(shù)據(jù)分析方法和思路上的差別進行了深入剖析。最后,基于完善大數(shù)據(jù)時代的抽樣,使抽樣與大數(shù)據(jù)有效地銜接,配合發(fā)展的目的,本文探討了兩個方面的問題,一是抽樣調(diào)查數(shù)據(jù)與大數(shù)據(jù)之間的融合和補充,二是把隨機抽樣調(diào)查技術(shù)和非隨機抽樣調(diào)查技術(shù)分別應(yīng)用到分析大數(shù)據(jù)的方法和過程中。
[Abstract]:As a sample survey technology that has been recognized and extended to the present since the end of the nineteenth century, it is not only a role in the acquisition of data and statistical inference, but also represents a small idea that will be used to make the data structure unchanged. However, under the age of large data, the continuous improvement of the storage and computing power has made people have the inexhaustible data resources, and the prediction method of closer results, people don't seem to need to set the whole, Instead of taking samples from the overall sample, the sample is typically extracted from the sample, instead of large data in the form of full data. So there are more and more people to ignore the sample survey, and it is believed that the sample survey in the big data age has not existed. Isn't there a need for sampling in the big data age? What are the challenges to sampling in the context of large data? How to make the sampling of the big data age? The writing of this paper is to explore and think about a series of problems, first, from the historical background of the birth of the sample survey, it is pointed out that the sample survey is produced in practice in order to represent the overall data with a small amount of data, The value of the research work in the course of its development is clarified. Then, combined with the characteristics of the large data age and the shortcomings of the sample survey, the challenges faced by the sample survey in the large data age are described, including the non-sampling error in the sample survey and the existence of the sampling error, and the difficulty in the overall estimation of the sub-population. Then, in order to explain the necessity of the sample survey in the age of large data, the concept of large data, general, large sample and full data is analyzed. At the same time, the source and characteristics of the large data and the sampling data and the difference between the data analysis method and the thought are analyzed. At last, based on the perfection of the sampling of the big data age, the sampling and the big data are effectively connected, and the purpose of the development is discussed in this paper. The first is the fusion and the supplement between the sample survey data and the big data. The second is to apply the random sampling technique and the non-random sampling technique to the method and the process of analyzing the big data, respectively.
【學(xué)位授予單位】：蘭州財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：C81

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