A calibrated imputation method for secondary data analysis of survey data
نویسندگان
چکیده
منابع مشابه
Missing data imputation in multivariable time series data
Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
متن کاملa new approach to credibility premium for zero-inflated poisson models for panel data
هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...
15 صفحه اولCalibrated Imputation of Numerical Data under Linear Edit Restrictions
A common problem faced by statistical institutes is that data may be missing from collected datasets. The typical way to overcome this problem is to impute the missing data. The problem of imputing missing data is complicated by the fact that statistical data often have to satisfy certain edit rules and that values of variables across units sometimes have to sum up to known totals. For numerica...
متن کاملFuzzy Data Envelopment Analysis for Classification of Streaming Data
The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...
متن کاملIntroducing a secondary goal for evaluating DMUs by cross efficiency in data envelopment analysis
One way to rank DMUs in DEA is the cross efficiency method. In this method, the efficiencyof each DMU is calculated by other DMUs optimum weights, which makes the ranking moreacceptable for managers. Existing alternative optimum weights in cross efficiency methodlead to several ranks for DMUs. Several secondary goals have introduced to avoid thisproblem, till now. In this paper, a new model is ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scandinavian Journal of Statistics
سال: 2019
ISSN: 0303-6898,1467-9469
DOI: 10.1111/sjos.12435