【摘要】：在互聯(lián)網(wǎng)大數(shù)據(jù)的背景下,縱向數(shù)據(jù)因其能將截面數(shù)據(jù)和時(shí)間序列數(shù)據(jù)有效的結(jié)合起來(lái),所以在網(wǎng)絡(luò)營(yíng)銷中占有非常重要的地位.特別在網(wǎng)絡(luò)營(yíng)銷中也經(jīng)常會(huì)由于高維數(shù)據(jù)的稀疏性,導(dǎo)致高維空間中的數(shù)據(jù)處理方法與低維空間中存在顯著差異,有必要進(jìn)一步解決和研究.傳統(tǒng)技術(shù)在大數(shù)據(jù)環(huán)境下并不能很好地對(duì)高維數(shù)據(jù)進(jìn)行研究,因此本文將新型的變量選擇方法與傳統(tǒng)的縱向數(shù)據(jù)結(jié)合,具體的研究?jī)?nèi)容和結(jié)果如下:首先,將Elastic Net方法應(yīng)用于網(wǎng)絡(luò)營(yíng)銷中經(jīng)常出現(xiàn)的縱向數(shù)據(jù),不僅能夠更好地理解大數(shù)據(jù)對(duì)各種營(yíng)銷活動(dòng)的影響,同時(shí)也讓企業(yè)更好地發(fā)揮其有效性.證明了平衡縱向數(shù)據(jù)模型的Elastic Net估計(jì)具有組效應(yīng)性質(zhì).數(shù)據(jù)模擬驗(yàn)證了 Elastic Net方法能將強(qiáng)相關(guān)變量全部選入縱向數(shù)據(jù)模型,而Lasso方法無(wú)此功效.其次,盡管Elastic Net方法相對(duì)于Lasso方法在處理強(qiáng)相關(guān)變量組問(wèn)題時(shí)具有顯著的優(yōu)越性,但二者均不具備Oracle性質(zhì).本文將Adaptive Elastic Net方法與縱向數(shù)據(jù)模型進(jìn)行結(jié)合,證明了 Adaptive Elastic Net方法能更有效的處理強(qiáng)相關(guān)變量和零變量,即具有組效應(yīng)性質(zhì)及Oracle性質(zhì).并通過(guò)數(shù)值模擬,表明了 Adaptive Elastic Net方法與縱向數(shù)據(jù)模型結(jié)合后的估計(jì)值明顯優(yōu)于Lasso方法和Elastic Net方法.最后,我們利用網(wǎng)絡(luò)營(yíng)銷中廣告點(diǎn)擊率的實(shí)際例子,將縱向數(shù)據(jù)與廣告點(diǎn)擊率相結(jié)合,利用Elastic Net方法進(jìn)行變量選擇,篩選出重要的關(guān)鍵詞,從而更好的提高廣告點(diǎn)擊率.表明Elastic Net方法用于縱向數(shù)據(jù)模型是可行的,且模型的擬合效果和預(yù)測(cè)能力均強(qiáng)于傳統(tǒng)的縱向數(shù)據(jù)模型.同時(shí)它還實(shí)現(xiàn)了 Elastic Net方法在網(wǎng)絡(luò)營(yíng)銷中的應(yīng)用.
[Abstract]:Under the background of Internet big data, vertical data play a very important role in network marketing because it can effectively combine cross-section data and time series data. Especially in network marketing, because of the sparsity of high-dimensional data, there are significant differences between the methods of data processing in high-dimensional space and low-dimensional space, so it is necessary to further solve and study. The traditional technology can not study the high-dimensional data well under the big data environment, so this paper combines the new variable selection method with the traditional longitudinal data. The specific research contents and results are as follows: first, Applying the Elastic Net method to the vertical data which often appears in the network marketing can not only better understand the influence of big data on various marketing activities, but also enable enterprises to give full play to its effectiveness. It is proved that the Elastic Net estimation of the balanced longitudinal data model has the property of group effect. The data simulation verifies that the Elastic Net method can select all the strongly correlated variables into the longitudinal data model, but the Lasso method does not. Secondly, although the Elastic Net method is superior to the Lasso method in dealing with the problem of strongly correlated variable sets, neither of them has the Oracle property. In this paper, the Adaptive Elastic Net method is combined with the longitudinal data model, and it is proved that the Adaptive Elastic Net method can deal with strong correlation variables and zero variables more effectively, that is, it has the property of group effect and Oracle property. Numerical simulation shows that the estimated value of Adaptive Elastic Net method combined with longitudinal data model is obviously better than that of Lasso method and Elastic Net method. Finally, we make use of the actual example of advertising click rate in network marketing, combine longitudinal data with ad click rate, use Elastic Net method to select variables, screen out important keywords, so as to improve the ad click rate better. It is shown that the Elastic Net method is feasible for the longitudinal data model, and the fitting effect and prediction ability of the model are better than that of the traditional longitudinal data model. At the same time, it also realizes the application of Elastic Net method in network marketing.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F274;F724.6;F224

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本文編號(hào)：2150905