In this paper we present a methodology for the study of multi-dimensional aspects of poverty and deprivation. The conventional poor/non-poor dichotomy is replaced by defining poverty as a matter of degree, determined by the place of the individual in the income distribution. The fuzzy poverty measure proposed is in fact also expressible in terms of the generalised Gini measure. The same methodology facilitates the inclusion of other dimensions of deprivation into the analysis: by appropriately weighting indicators of deprivation to reflect their dispersion and correlation, we can construct measures of non-monetary deprivation in its various dimensions. These indicators illuminate the extent to which purely monetary indicators are insufficient in themselves in capturing the prevalence of deprivation. An important contribution of the paper is to identify rules for the aggregation of fuzzy sets appropriate for the study of poverty and deprivation. In particular, we define a ‘composite’ fuzzy set operator which takes into account whether the sets being aggregated are of a ‘similar’ or a ‘dissimilar’ type. These rules allow us to meaningfully combine income and the diverse non-income deprivation indices at the micro-level and construct what we have termed ‘intensive’ and ‘extensive’ indicators of deprivation. We note that mathematically the same approach can be carried over to the study of persistence of poverty and deprivation over time

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A systematic procedure for the derivation of linearized variables for the estimation of sampling errors of\ud complex nonlinear statistics involved in the analysis of poverty and income inequality is developed. The\ud linearized variable extends the use of standard variance estimation formulae, developed for linear statistics\ud such as sample aggregates, to nonlinear statistics. The context is that of cross-sectional samples of complex\ud design and reasonably large size, as typically used in population-based surveys. Results of application of\ud the procedure to a wide range of poverty and inequality measures are presented. A standardized software\ud for the purpose has been developed and can be provided to interested users on request. Procedures are\ud provided for the estimation of the design effect and its decomposition into the contribution of unequal\ud sample weights and of other design complexities such as clustering and stratification. The consequence of\ud treating a complex statistic as a simple ratio in estimating its sampling error is also quantified. The second\ud theme of the paper is to compare the linearization approach with an alternative approach based on the\ud concept of replication, namely the Jackknife repeated replication (JRR) method. The basis and application\ud of the JRR method is described, the exposition paralleling that of the linearization method but in somewhat\ud less detail. Based on data from an actual national survey, estimates of standard errors and design effects\ud from the two methods are analysed and compared. The numerical results confirm that the two alternative\ud approaches generally give very similar results, though notable differences can exist for certain statistics.\ud Relative advantages and limitations of the approaches are identified

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"Sampling errors and design effects from 48 nationally representative surveys conducted under the Demographic and Health Surveys Program for a large number of variables concerning fertility, family planning, fertility intentions, child health and mortality etc. are analysed for the total sample, and for urban-rural domains, sub-national regions and various demographic and socio-economic subclasses.... At the country level, overall design effect (the ratio of actual to simple random sampling standard error) averaged over all variables and countries is around 1.5. Variation among countries is high, but less so than among variables. Urban-rural and regional differentials in design effects are small, and can be attributed to the fact that similar sample designs and cluster sizes were used across those domains within each country. Design effects for estimates over other subclasses are smaller, and tend towards 1.0 for small subclasses and differences, apart from the effect of sample weights which tends to persist undiminished across variables and subclasses." (SUMMARY IN FRE)

Summary In this paper, we present a practical methodology for variance estimation for multi‐dimensional measures of poverty and deprivation of households and individuals, derived from sample surveys with complex designs and fairly large sample sizes. The measures considered are based on fuzzy representation of individuals' propensity to deprivation in monetary and diverse non‐monetary dimensions. We believe this to be the first original contribution for estimating standard errors for such fuzzy poverty measures. The second objective is to describe and numerically illustrate computational procedures and difficulties in producing reliable and robust estimates of sampling error for such complex statistics. We attempt to identify some of these problems and provide solutions in the context of actual situations. A detailed application based on European Union Statistics on Income and Living Conditions data for 19 NUTS2 regions in Spain is provided.

This study condenses huge amount of raw data measured from a MEMS accelerometer-based, wrist-worn device on different levels of physical activities (PAs) for subjects wearing the device 24 h a day continuously. In this study, we have employed the device to build up assessment models for quantifying activities, to develop an algorithm for sleep duration detection and to assess the regularity of activity of daily living (ADL) quantitatively. A new parameter, the activity index (AI), has been proposed to represent the quantity of activities and can be used to categorize different PAs into 5 levels, namely, rest/sleep, sedentary, light, moderate, and vigorous activity states. Another new parameter, the regularity index (RI), was calculated to represent the degree of regularity for ADL. The methods proposed in this study have been used to monitor a subject’s daily PA status and to access sleep quality, along with the quantitative assessment of the regularity of activity of daily living (ADL) with the 24-h continuously recorded data over several months to develop activity-based evaluation models for different medical-care applications. This work provides simple models for activity monitoring based on the accelerometer-based, wrist-worn device without trying to identify the details of types of activity and that are suitable for further applications combined with cloud computing services.