Personal assistance: what happens to the arrangement when the number of users increases and new user groups are included?

Using data from two representative surveys among the users of personal assistance in Norway carried out in 2002 and 2010, this paper examines developments and consequences of a strong increase of users and an extension of the target group. Users with mobility impairments still dominate, but the proportion of people with intellectual impairments, brain injuries, and sensory impairments have increased. The ‘new' users seem to be allocated fewer hours compared with those who received personal assistance at the early stages of the arrangement. Still, most users experience an increase in their welfare arrangements, as compared with the situation before they received personal assistance. The user control of the arrangement seems to be preserved, but it takes more different forms. For a higher proportion of users, one of their relatives or a guardian acts as a manager of the assistance.     

The null hypothesis of (common) jumps in case of irregular and asynchronous observations

This paper proposes novel tests for the absence of jumps in a univariate semimartingale and for the absence of common jumps in a bivariate semimartingale. Our methods rely on ratio statistics of power variations based on irregular observations, sampled at different frequencies. We develop central limit theorems for the statistics under the respective null hypotheses and apply bootstrap procedures to assess the limiting distributions. Furthermore, we define corrected statistics to improve the finite sample performance. Simulations show that the test based on our corrected statistic yields good results and even outperforms existing tests in the case of regular observations.     

Scaling limits for the transient phase of local Metropolis–Hastings algorithms

Summary.  The paper considers high dimensional Metropolis and Langevin algorithms in their initial transient phase. In stationarity, these algorithms are well understood and it is now well known how to scale their proposal distribution variances. For the random-walk Metropolis algorithm, convergence during the transient phase is extremely regular—to the extent that the algo-rithm's sample path actually resembles a deterministic trajectory. In contrast, the Langevin algorithm with variance scaled to be optimal for stationarity performs rather erratically. We give weak convergence results which explain both of these types of behaviour and practical guidance on implementation based on our theory.     

Mapping the regional transition to cyclicity inClethrionomys rufocanus: Spectral densities and functional data analysis

We study the regional transitions in dynamics of the gray-sided vole,Clethrionomys rufocanus, within Hokkaido, Japan. The data-set consists of 225 time series of varying length (most from 23 to 31 years long) collected between 1962 and 1992 by the Forestry Agency of the Japanese Government. To see clearly how the periodic behavior changes geographically, we estimate the spectral density functions of the growth rates of all populations using a log-spline method. We subsequently apply functional data analysis to the estimated densities. The functional data analysis is, in this context, analogous to a principal component analysis applied to curves. We plot the results of the analysis on the map of Hokkaido, to reveal a clear transition from relatively stable populations in the southwest and west to populations undergoing 3–4 year cycles in the northeast and east. The degree of seasonality in the vegetation and the rodent demography appear to be strongest in the cyclic area. We briefly speculate that the destabilization of the rodent dynamics is linked to increased seasonalforcing on the trophic interactions in which the gray-sided voles are involved.     

Partial least squares Cox regression for genome-wide data

Most methods for survival prediction from high-dimensional genomic data combine the Cox proportional hazards model with some technique of dimension reduction, such as partial least squares regression (PLS). Applying PLS to the Cox model is not entirely straightforward, and multiple approaches have been proposed. The method of Park et al. (Bioinformatics 18(Suppl. 1):S120–S127, 2002) uses a reformulation of the Cox likelihood to a Poisson type likelihood, thereby enabling estimation by iteratively reweighted partial least squares for generalized linear models. We propose a modification of the method of park et al. (2002) such that estimates of the baseline hazard and the gene effects are obtained in separate steps. The resulting method has several advantages over the method of park et al. (2002) and other existing Cox PLS approaches, as it allows for estimation of survival probabilities for new patients, enables a less memory-demanding estimation procedure, and allows for incorporation of lower-dimensional non-genomic variables like disease grade and tumor thickness. We also propose to combine our Cox PLS method with an initial gene selection step in which genes are ordered by their Cox score and only the highest-ranking k% of the genes are retained, obtaining a so-called supervised partial least squares regression method. In simulations, both the unsupervised and the supervised version outperform other Cox PLS methods.     

Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise

This paper shows how to use realized kernels to carry out efficient feasible inference on the ex post variation of underlying equity prices in the presence of simple models of market frictions. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which equals that of the maximum likelihood estimator in the parametric version of this problem. Realized kernels can also be selected to (i) be analyzed using endogenously spaced data such as that in data bases on transactions, (ii) allow for market frictions which are endogenous, and (iii) allow for temporally dependent noise. The finite sample performance of our estimators is studied using simulation, while empirical work illustrates their use in practice.     

Reaching universal coverage by means of social health insurance in Lesotho? Results and implications from a financial feasibility assessment

This article discusses the process, results and implications of a financial feasibility assessment of social health insurance (SHI), as one part of Lesotho's exploration of how to move towards achieving universal health care coverage. Quantitative data from government and other sources, and qualitative data from discussions with stakeholders, were entered into SimIns, a health insurance simulation software, through which SHI revenue and expenditure for 11 years was projected. In principle, the assessment reveals that through a mix of tax financing and SHI contributions, all citizens of Lesotho could be covered with a defined benefit package of health services under the defined policy assumptions. Such a financing scheme would provide financial risk protection and enhance equity in access and health financing.