000 02217cam  2200313za 4500
0019.834769
003CaOODSP
00520221107150517
007cr |||||||||||
008170404s2017    oncd    ob   f000 0 eng d
040 |aCaOODSP|beng
041 |aeng|bfre
043 |an-cn---
0861 |aFB3-1/107-2017E-PDF
1001 |aHatko, Stan.
24514|aThe Bank of Canada 2015 retailer survey on the cost of payment methods |h[electronic resource] : |bnonresponse / |cStan Hatko.
260 |a[Ottawa] : |bBank of Canada, |c2017.
300 |aii, 45 p. : |bcol. charts
4901 |aTechnical report = Rapport technique, |x1919-689X ; |vno. 107
500 |a"March 2017."
504 |aIncludes bibliographical references (p. 21-22).
5203 |a"Nonresponse is a considerable challenge in the Retailer Survey on the Cost of Payment Methods conducted by the Bank of Canada in 2015. There are two types of nonresponse in this survey: unit nonresponse, in which a business does not reply to the entire survey, and item nonresponse, in which a business does not respond to particular questions within the survey. Both types may create a bias when computing statistics such as means and weighted totals for different variables. This technical report analyzes solutions to fix the problem of nonresponse in the survey data. Unit nonresponse is addressed through response probability adjustment, in which response probabilities are modelled using logistic regression (a clustering approach for the unit response probabilities is also considered) and are used in the construction of a set of survey weights. Item nonresponse is addressed through imputation, in which the gradient boosting machine (GBM) and extreme gradient boosting (XGBoost) algorithms are used to predict missing values for variables of interest"--Abstract, p. ii.
546 |aIncludes abstract in French.
69207|2gccst|aRetail trade
69207|2gccst|aFinance
69207|2gccst|aSurveys
7102 |aBank of Canada.
830#0|aTechnical report (Bank of Canada)|x1919-689X ; |vno. 107|w(CaOODSP)9.505019
85640|qPDF|s645 KB|uhttps://publications.gc.ca/collections/collection_2017/banque-bank-canada/FB3-1-107-2017-eng.pdf