Non-Sampling Errors in Household Surveys: The Bank of Italy’s Experience

2012 ◽  
pp. 103-118
Author(s):  
Giovanni D’Alessio ◽  
Giuseppe Ilardi
Keyword(s):  
Household Surveys ◽  
Sampling Errors

On the Use of Auxiliary and Paradata for Dealing With Non-sampling Errors in Household Surveys

2013 ◽  
Vol 81 (2) ◽  
pp. 270-288 ◽  
Author(s):  
Li-Chun Zhang ◽  
Ib Thomsen ◽  
Øyvin Kleven
Keyword(s):  
Household Surveys ◽  
Sampling Errors

scholarly journals Errors of Indices in Household Surveys of Punjab Urban through Principal Components

2021 ◽  
Vol 9 (1) ◽  
pp. 51-58
Author(s):  
Muhammad Imran ◽  
Fatima Seher Zaidi
Keyword(s):  
Principal Components ◽  
Economic Status ◽  
Orthogonal Transformation ◽  
Household Survey ◽  
Simple Random Sampling ◽  
Complete Information ◽  
Household Wealth ◽  
Published Data ◽  
Household Surveys ◽  
Sampling Errors

In this research article, household wealth indices are calculated to estimate the sampling errors, which gave us complete information on the quality and reliability of published data upon the household surveys. Estimates are calculated based on simple random sampling, which contains sampling errors. Here principal components analysis (PCA) estimate standard errors of wealth indices as orthogonal transformation to develop solid measures of individual economic status. These measures evaluate the significance and explain the living status and economic dissimilarity of Punjab urban. Instrumental variables are used here to the enlightened social status of the Punjab urban area of Pakistan by using PCA of household surveys.  Twenty-five variables are included in this study. Total variance analysis explains the variation of total components.  A comparison study (PCA) approaches to estimating the standard error of indices of the household survey are presented in this paper. We conclude that errors of indices in household surveys through PCA, when compared to direct measures of estimating household wealth indices, are an efficient and reliable method.

Well-being and inequality in comparative perspective: the case of Poland and Ukraine

2019 ◽  
pp. 32-55
Author(s):  
Natalia Kovalisko ◽  
Serhii Makeev
Keyword(s):  
Tax Evasion ◽  
Shadow Economy ◽  
Free Market ◽  
Well Being ◽  
Household Surveys ◽  
Cheap Labour ◽  
Catching Up ◽  
Public Opinion Polls ◽  
Opinion Polls

Socio-economic trajectories of Poland and Ukraine have been considerably diverging since the last decade of the 20th century. The former has been advancing and catching up with Western European countries in terms of the quality of life — whereas in Ukraine, the 1990s recession gave way to unsustainable economic growth, which interrupted in the second half of the 2000s and in the 2010s. The comparison of official statistics, along with the data of household surveys and public opinion polls, makes it possible to conclude that a progressive and sustainable transition from a command economy to free market, as exemplified by Poland, is accompanied by moderate deepening of economic inequality. However, an abnormal transition (deviating from the “Polish rule”) entails excessive concentration of wealth and gives rise to corruption as a mechanism of income redistribution among different categories of population. This also results in a more noticeable stratification of opportunies for meeting vital and existential needs. Owing to a large proportion of shadow economy and undeclared work, Ukrainians remain a source of cheap labour in both the domestic and international labour markets; in addition, a persistent subculture of tax evasion is being formed in this country.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

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