Osa-aikaeläkeläisten työuran pituus vertailussa kokoaikatyössä jatkaneisiin

Peer reviewe

Osa-aikaeläkeläisten työuran pituus vertailussa kokoaikatyössä jatkaneisiin

Peer reviewe

Using sequence analysis to visualize and validate model transitions

Peer reviewe

Modelling old-age retirement : An adaptive multi-outcome LAD-lasso regression approach

Using unique administrative register data, we investigate old-age retirement under the statutory pension scheme in Finland. The analysis is based on multi-outcome modelling of pensions and working lives together with a range of explanatory variables. An adaptive multi-outcome LAD-lasso regression method is applied to obtain estimates of earnings and socioeconomic factors affecting old-age retirement and to decide which of these variables should be included in our model. The proposed statistical technique produces robust and less biased regression coefficient estimates in the context of skewed outcome distributions and an excess number of zeros in some of the explanatory variables. The results underline the importance of late life course earnings and employment to the final amount of pension and reveal differences in pension outcomes across socioeconomic groups. We conclude that adaptive LAD-lasso regression is a promising statistical technique that could be usefully employed in studying various topics in the pension industry.Peer reviewe

OjaNP: Multivariate Methods Based on the Oja Median and Related Concepts

Functions to calculate the Oja median, Oja signs and ranks and methods based upon them.201

Impact of missing data mechanism on the estimate of change: a case study on cognitive function and polypharmacy among older persons

Longitudinal studies typically suffer from incompleteness of data. Attrition is a major problem in studies of older persons since participants may die during the study or are too frail to participate in follow-up examinations. Attrition is typically related to an individual’s health; therefore, ignoring it may lead to too optimistic inferences, for example, about cognitive decline or changes in polypharmacy. The objective of this study is to compare the estimates of level and slope of change in 1) cognitive function and 2) number of drugs in use between the assumptions of ignorable and non-ignorable missingness. This study demonstrates the usefulness of latent variable modeling framework. The results suggest that when the missing data mechanism is not known, it is preferable to conduct analyses both under ignorable and non-ignorable missing data assumptions.peerReviewe