The determinants and productivity growth are analyzed by using regression techniques for limited dependent variable models with random effects 然后,運用虛擬因變量方法分析影響全要素生產(chǎn)率變化的具體因素。
The safe selective regression technique not only guarantees the quality of the project but also reduces the cost and the regression test suite 該技術(shù)在保證軟件質(zhì)量的基礎(chǔ)上有效地降低了測試花銷,減小了回歸測試用例集的規(guī)模。
This paper discusses the modem regression techniques used for ordinal data analysis and how to apply them into solving an important sampling problem in consumer confidence index survey 摘要本文討論了對定序變量進(jìn)行回歸的技術(shù),并探討了如何用其解決消費者信心指數(shù)調(diào)查中抽樣的重要問題。
Then it gives some advices about raising the tfp . the dissertation ' s main contribution lies in using malmquist - dea method and regression techniques for limited dependent variable models with random effects 本論文的主要貢獻(xiàn)在于把擴(kuò)展的malmqust dea方法引入交通運輸業(yè)并運用虛擬因變量回歸模型考察全要素生產(chǎn)率變化的影響因素。
At the same time , the method of creating the test cases of the multilayer functional model is also mentioned . the characteristics and disadvantages of some regression testing techniques are discussed , on which we bring out the safe selective regression technique 在分析并討論了現(xiàn)有軟件回歸測試技術(shù)特點和存在的問題的基礎(chǔ)上,提出了基于多層次功能模型的安全選擇回歸測試技術(shù)。
The safe selective regression technique based on multilayer functional model was carried out in the project of the china union pay system integration te - sting - the switch subsystem functional testing . it is indicated that the technique is flexible , effective and practicable 最后本文結(jié)合中國銀聯(lián)信息處理中心系統(tǒng)集成測試-轉(zhuǎn)接子系統(tǒng)功能測試測試實例,說明該技術(shù)是一項靈活有效并且十分實用的回歸測試技術(shù)。
Global motion can be modeled by a few parameters will be stated in chapter 2 . in chapter two are given the system structure and key technologies of global estimation and compensation , describing several possible details of each key technology . global motion parameters estimated using regression technique which first estimate the local motion and then uses the local information to find the global motion that minimize the least square error 并重點研究了一種基于回歸分析的圖像全局運動估計與補(bǔ)償技術(shù),它首先利用光流場法估計局部圖像背景點的速度場,然后利用魯棒的疊代排除法估計圖像傳感器的全局運動模型參數(shù),再利用估計出來的全局運動參數(shù)對圖像進(jìn)行雙線性內(nèi)插運動補(bǔ)償。
Today , the third party of software testing is taking part in testing process . as the functional testing is very important for the third part of software testing , and the test for relationship between operations has attached importance , it is practical to investigate the regression technique based on function and operation specifications 現(xiàn)階段,軟件測試第三方逐漸介入到軟件測試過程中,功能測試是其中一項重要內(nèi)容,而且對業(yè)務(wù)流程間聯(lián)系的測試漸漸受到重視,所以基于功能的、同時考慮業(yè)務(wù)需求的回歸測試技術(shù)研究具有重要的現(xiàn)實意義。
Up to now , there are many software regression testing techniques , such as retest all regression technique , random select regression technique , minimization regression technique , data flow regression technique , safe regression technique etc . . however , all these techniques are code - based 到目前為止,已經(jīng)有很多軟件回歸測試技術(shù),其中具有代表性的幾種技術(shù)是全部回歸測試技術(shù),隨機(jī)選擇回歸測試技術(shù),最小化回歸測試技術(shù),數(shù)據(jù)流回歸測試技術(shù),安全回歸測試技術(shù)等。
Evidence suggests that the prognostic ability of the new model with high stability , when hidden nodes changing nearby input nodes and training times changing at the certain extent , is significantly better than traditional step wise regression model mainly due to the new model condensing the more forecasting information , properly utilizing the ability of ann self - adaptive learning and nonlinear mapping . but the linear regression technique only selects several predictors by the f value , many predictors information with high relative coefficients is not included . so the new model proposed in this paper is effective and is of a very good prospect in the atmospheric sciences fields 進(jìn)一步深入分析研究發(fā)現(xiàn),本文提出的這種基于主成分的神經(jīng)網(wǎng)絡(luò)預(yù)報模型,預(yù)報精度明顯高于傳統(tǒng)的逐步回歸方法,其主要原因是這種新的預(yù)報模型集中了眾多預(yù)報因子的預(yù)報信息,并有效地利用了人工神經(jīng)網(wǎng)絡(luò)方法的自組織和自適應(yīng)的非線性映射能力;而傳統(tǒng)的逐步回歸方法是一種線性方法,并且逐步回歸方法只是根據(jù)f值大小從眾多預(yù)報因子中選取幾個預(yù)報因子,其余預(yù)報因子的預(yù)報信息被舍棄。