Teknisk rapport for pilotundersøkelsen av forsøpling i Kristiansand kommune

Søppelundersøkelser har blitt gjennomført i Sverige i rundt ti år i samarbeid med Hold Sverige Rent (HSR), Statistiska centralbyrå (SCB) og svenske kommuner. I 2017 ble det planlagt å gjennomføre en slik undersøkelse i Norge under ledelse av Hold Norge Rent (HNR) for å kartlegge forsøpling i norske kommuner. Målet med å gjennomføre en slik undersøkelse er å overvåke forekomsten av søppel for deretter å gjøre nødvendige tiltak mot forsøpling. Det ble bestemt at en pilotundersøkelse skulle gjennomføres i Kristiansand kommune. Målet med pilotundersøkelsen var å finne ut hvordan man kan kartlegge mengden av søppel i norske kommuner. Den svenske metoden ble brukt uten noen endringer, bortsett fra stratifiseringen av utvalgsområdene. Områdene som måles kan deles inn i to grupper: i) gateområder og ii) parker og grønne områder. Gravaneparken og en fjerdedel av Kvadraturen i Kristiansand ble valgt som målområder for henholdsvis park- og gatemålinger. Målet var å gi estimater for mengde søppel av ulike typer per ti kvadratmeter. Utvalgsramme, utvalgsenhet, spørreskjema og måten å samle inn data på er forskjellig for de to områdene. Denne rapporten gir en oversikt over hele metodologien som ble brukt i pilotundersøkelsen, samt hensyn og anbefalinger angående ulike deler av metodikken. Undersøkelsesresultater angående de valgte områdene presenteres også. Man må imidlertid være klar over at dette er basert på en begrenset mengde data. Derfor kan de statistiske tallene ha stor usikkerhet.publishedVersio

BIG sampling

Graph sampling is a statistical approach to study real graphs, which represent the structure of many technological, social or biological phenomena of interest. We develop bipartite incident graph sampling (BIGS) as a feasible representation of graph sampling from arbitrary finite graphs. It provides also a unified treatment of the existing unconventional sampling methods which were studied separately in the past, including indirect, network and adaptive cluster sampling. The sufficient and necessary conditions of feasible BIGS representation are established, given which one can apply a family of Hansen-Hurwitz type design-unbiased estimators in addition to the standard Horvitz-Thompson estimator. The approach increases therefore the potentials of efficiency gains in graph sampling. A general result regarding the relative efficiency of the two types of estimators is obtained. Numerical examples are given to illustrate the versatility of the proposed approach

Empirical likelihood confidence intervals and significance test for regression parameters under complex sampling designs

Confidence intervals based on ordinary least squares may have poor coverages for regression parameters when the effect of sampling design is ignored. Standard confidence intervals based on design variances may not have the right coverages when the sampling distribution is skewed. Berger and De La Riva Torres (2012) proposed an empirical likelihood approach which can be used for point estimation and to construct confidence intervals under complex sampling designs for a single parameter. We show that this approach can be extended to test the significance of a subset of model parameters and to derive confidence intervals. The proposed approach is not a straightforward extension of Berger and De La Riva Torres (2012) approach, because we consider the situation when the parameter is multidimensional and the parameter of interest is a subset of the parameter. This requires profiling which is not covered by Berger and De La Riva Torres (2012). The proposed approach intrinsically incorporates sampling weights, design variables, and auxiliary information. It may yield to more accurate confidence intervals when the sampling distribution of the regression parameters is not normal, the point estimator is biased, or the regression model is not linear. The proposed approach is simple to implement and less computer intensive than bootstrap. The proposed approach does not rely on re-sampling, linearisation, variance estimation, or design-effect

Variance estimation of change of poverty based upon the Turkish EU-SILC survey

Interpreting changes between point estimates at different waves may be misleading, if we do not take the sampling variation into account. It is therefore necessary to estimate the standard error of these changes in order to judge whether or not the observed changes are statistically significant. This involves the estimation of temporal correlations between cross sectional estimates, because correlations play an important role in estimating the variance of change in the cross-sectional estimates. Standard estimator for correlations cannot be used, because of the rotation used in most panel surveys, such as the European Union Statistics on Income and Living Conditions (EU-SILC) surveys. Furthermore, as poverty indicators are complex functions of the data, they need a special treatment when estimating their variance. For example, poverty rates depend on poverty thresholds which are estimated from medians. We propose to use a multivariate linear regression approach to estimate correlations by taking into account of the variability of the poverty threshold. We apply the proposed approach to the Turkish EU-SILC survey data

New estimation methodology for the Norwegian Labour Force Survey

Labour Force Survey (LFS) is an important source of the labour market statistics that provides information about the participation of people aged 15 and over in to the labour market and people outside of the labour market. It is a rotating panel sample survey that is carried out in accordance with the European Union (EU) Council Regulation. Statistics produced are subject to both sampling and non–response errors. Sampling errors are monitored through standard errors, which are provided alongside with the point estimates for the key variables. In that respect, finding an efficient estimator is one of the main goals for the LFS. This requires data sources that includes good auxiliary variables. Thus we aim to find an estimation methodology which better utilises the auxiliary information in the light of a new available data source, namely A–ordningen. In this regard, we compare the regular generalised regression estimator (GREG) and the (multiple) model–calibration estimator, which has been shown to be optimal among a class of calibration estimators, in terms of efficiency by using the Norwegian LFS data. Standard errors are estimated by using the Jackknife linearisation (JL) variance estimator. Overall, for the data used, the (multiple) model–calibration estimators have been more efficient than than the GREG estimators. Thus the former has been chosen to be used in the production of the Norwegian labour force statistics

Teknisk rapport for pilotundersøkelsen av forsøpling i Kristiansand kommune

Søppelundersøkelser har blitt gjennomført i Sverige i rundt ti år i samarbeid med Hold Sverige Rent (HSR), Statistiska centralbyrå (SCB) og svenske kommuner. I 2017 ble det planlagt å gjennomføre en slik undersøkelse i Norge under ledelse av Hold Norge Rent (HNR) for å kartlegge forsøpling i norske kommuner. Målet med å gjennomføre en slik undersøkelse er å overvåke forekomsten av søppel for deretter å gjøre nødvendige tiltak mot forsøpling. Det ble bestemt at en pilotundersøkelse skulle gjennomføres i Kristiansand kommune. Målet med pilotundersøkelsen var å finne ut hvordan man kan kartlegge mengden av søppel i norske kommuner. Den svenske metoden ble brukt uten noen endringer, bortsett fra stratifiseringen av utvalgsområdene. Områdene som måles kan deles inn i to grupper: i) gateområder og ii) parker og grønne områder. Gravaneparken og en fjerdedel av Kvadraturen i Kristiansand ble valgt som målområder for henholdsvis park- og gatemålinger. Målet var å gi estimater for mengde søppel av ulike typer per ti kvadratmeter. Utvalgsramme, utvalgsenhet, spørreskjema og måten å samle inn data på er forskjellig for de to områdene. Denne rapporten gir en oversikt over hele metodologien som ble brukt i pilotundersøkelsen, samt hensyn og anbefalinger angående ulike deler av metodikken. Undersøkelsesresultater angående de valgte områdene presenteres også. Man må imidlertid være klar over at dette er basert på en begrenset mengde data. Derfor kan de statistiske tallene ha stor usikkerhet

New estimation methodology for the Norwegian Labour Force Survey

Labour Force Survey (LFS) is an important source of the labour market statistics that provides information about the participation of people aged 15 and over in to the labour market and people outside of the labour market. It is a rotating panel sample survey that is carried out in accordance with the European Union (EU) Council Regulation. Statistics produced are subject to both sampling and non–response errors. Sampling errors are monitored through standard errors, which are provided alongside with the point estimates for the key variables. In that respect, finding an efficient estimator is one of the main goals for the LFS. This requires data sources that includes good auxiliary variables. Thus we aim to find an estimation methodology which better utilises the auxiliary information in the light of a new available data source, namely A–ordningen. In this regard, we compare the regular generalised regression estimator (GREG) and the (multiple) model–calibration estimator, which has been shown to be optimal among a class of calibration estimators, in terms of efficiency by using the Norwegian LFS data. Standard errors are estimated by using the Jackknife linearisation (JL) variance estimator. Overall, for the data used, the (multiple) model–calibration estimators have been more efficient than than the GREG estimators. Thus the former has been chosen to be used in the production of the Norwegian labour force statistics

Variance estimation of change in poverty rates and empirical likelihood inference in the presence of nuisance parameters under complex sampling designs

This thesis includes three papers. The first paper demonstrates how to estimate variance of change in poverty rates under rotating complex sampling designs. Measuring variance of change enables practitioners to judge whether or not the observed changes over time are statistically significant. The main difficulty in estimation of variance of change under rotating designs arises in the estimation of correlations between cross sectional estimates. This paper addresses a multivariate linear regression approach that provides a valid correlation estimator. Furthermore, poverty rate is a complex statistic that depends on a poverty threshold, which is estimated from the survey data. The paper mainly contributes by taking into account the variability of the poverty threshold in variance estimation of change. The approach is applied to the Turkish eu-silc survey data. The second paper presents a design based inference in the presence of nuisance parameters by using an empirical likelihood approach. The main contribution of the paper is to develop an asymptotic theory to support the approach. The approach proposed can be used for testing and confidence intervals for finite population parameters such as (non)linear (generalised) regression parameters. For example, when comparing two nested models, the additional parameters are the parameters of interest, and the common parameters are the nuisance parameters. Sampling design and population level information are taken into account with the approach. Confidence intervals do not rely on resampling, linearisation, variance estimation, or design effects. The third paper shows how the empirical likelihood approach proposed in the second paper is applied to make inferences for regression coefficients when modelling hierarchical data collected from a two-stage sampling design where the first stage units may be selected with un-equal probabilities. Multilevel regressions are often employed in social sciences to analyse data with hierarchical structure. This paper considers fixed effect regression parameters that can be defined through `general estimating equations'. We use an `ultimate cluster approach' by treating the first stage sample units as the units of interest

Weighting methodology for the Norwegian Labour Force Survey from 2021 onwards

A new revision in the weighting procedure of the Norwegian Labour Force Survey since the last one made in 2018 was required due to the change in the target age group for employment statistics, the introduction of the new sampling design in 2021 and the new weighting requirements with the new EU legislation for labour force surveys within the EU. Compared to the revision in 2018, the main weighting method via model-calibration has remained unchanged for the age group 15 − 74. How ever, significant changes occurred in the calculation of selection probabilities, which is not straight forward, and initial weights to calibration due to the new sampling design, changes in the data struc ture and the new weighting requirements by the new EU legislation. A linear calibration method was introduced for those within age groups 0 − 14 and 75+. In order to fulfil the individual- and household-level requirements simultaneously and to ensure same weights for all individuals within any given household, an integrative calibration approach was introduced to be used for the calcula tion of yearly household weights. Several models were evaluated in this respect, and a final model was chosen by considering robustness and gain in precision

Weighting methodology for the Norwegian Labour Force Survey from 2021 onwards

A new revision in the weighting procedure of the Norwegian Labour Force Survey since the last one made in 2018 was required due to the change in the target age group for employment statistics, the introduction of the new sampling design in 2021 and the new weighting requirements with the new EU legislation for labour force surveys within the EU. Compared to the revision in 2018, the main weighting method via model-calibration has remained unchanged for the age group 15 - 74. However, significant changes occurred in the calculation of selection probabilities, which is not straightforward, and initial weights to calibration due to the new sampling design, changes in the data structure and the new weighting requirements by the new EU legislation. A linear calibration method was introduced for those within age groups 0 -14 and 75+. In order to fulfil the individual- and household-level requirements simultaneously and to ensure same weights for all individuals within any given household, an integrative calibration approach was introduced to be used for the calculation of yearly household weights. Several models were evaluated in this respect, and a final model was chosen by considering robustness and gain in precision