Ingrid Svensson

A Method to Estimate Fibre Length Distribution in Conifers Based on Wood Samples from Increment Cores

Summary We propose a method to estimate fibre length distribution in conifers based on wood samples from increment cores processed by automatic optical fibre-analysers. Automatic fibre-analysers are unable to distinguish: a) fibres from other tissues, “fines”, and b) cut from uncut fibres. However, our proposed method can handle these problems if the type of distributions that fibre lengths and fines follow is known. In our study the length distributions of fines and fibres were assumed to follow truncated normal distributions, characterised by means and standard deviations of the two distributions. Parameter estimates were obtained by the maximum likelihood method. Wood samples from two 22-year-old Scots pine trees at breast height were used to evaluate the performance of the method. From stem discs at 1.5 m, adjacent samples of 5 mm increment cores and wood pieces were taken. The cores were trimmed 1 mm at each side and samples were, after maceration, analysed in a Kajaani FiberLab 3.0. The results showed that the method works well and gives a possibility to distinguish fine and fibre length distribution.

Adjusting for fibre length-biased sampling probability using increment cores from standing trees

In a previous article (Mörling et al. 2003), we described a method to estimate the true fibre length distribution of conifers based on wood samples from 5-mm increment cores. However, the length bias problem arising from the fact that longer cells are more likely to be sampled in an increment core was neglected. The calculated true celllength distribution in the standing tree described in the quoted paper corresponded to the cell length distribution of those cells that at least partially appear in the increment core. This led to underestimation of the proportion of short fibres and overestimation of the proportion of long fibres. In the present study we demonstrate how to correct for this length bias. We used the same data set as presented previously to demonstrate the effect of the refined method.

Causal Effects of the Timing of Life-course Events Age at Retirement and Subsequent Health

In this article, we combine the extensive literature on the analysis of life-course trajectories as sequences with the literature on causal inference and propose a new matching approach to investigate the causal effect of the timing of life-course events on subsequent outcomes. Our matching approach takes into account pre-event confounders that are both time-independent and time-dependent as well as life-course trajectories. After matching, treated and control individuals can be compared using standard statistical tests or regression models. We apply our approach to the study of the consequences of the age at retirement on subsequent health outcomes, using a unique data set from Swedish administrative registers. Once selectivity in the timing of retirement is taken into account, effects on hospitalization are small, while early retirement has negative effects on survival. Our approach also allows for heterogeneous treatment effects. We show that the effects of early retirement differ according to preretirement income, with higher income individuals tending to benefit from early retirement, while the opposite is true for individuals with lower income.