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  • 1.
    Allévius, Benjamin
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Höhle, Michael
    Stockholm University, Faculty of Science, Department of Mathematics.
    An unconditional space–time scan statistic for ZIP‐distributed data2019In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 46, no 1, p. 142-159Article in journal (Refereed)
    Abstract [en]

    A scan statistic is proposed for the prospective monitoring of spatiotemporal count data with an excess of zeros. The method that is based on an outbreak model for the zero‐inflated Poisson distribution is shown to be superior to traditional scan statistics based on the Poisson distribution in the presence of structural zeros. The spatial accuracy and the detection timeliness of the proposed scan statistic are investigated by means of simulation, and an application on the weekly cases of Campylobacteriosis in Germany illustrates how the scan statistic could be used to detect emerging disease outbreaks. An implementation of the method is provided in the open‐source R package scanstatistics available on the Comprehensive R Archive Network.

  • 2. Bauder, David
    et al.
    Bodnar, Rostyslav
    Bodnar, Taras
    Stockholm University, Faculty of Science, Department of Mathematics.
    Schmid, Wolfgang
    Bayesian estimation of the efficient frontier2019In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 46, no 3, p. 802-830Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the estimation of the three determining parameters of the efficient frontier, the expected return, and the variance of the global minimum variance portfolio and the slope parameter, from a Bayesian perspective. Their posterior distribution is derived by assigning the diffuse and the conjugate priors to the mean vector and the covariance matrix of the asset returns and is presented in terms of a stochastic representation. Furthermore, Bayesian estimates together with the standard uncertainties for all three parameters are provided, and their asymptotic distributions are established. All obtained findings are applied to real data, consisting of the returns on assets included into the S&P 500. The empirical properties of the efficient frontier are then examined in detail.

  • 3.
    Björkstrom, Anders
    Stockholm University, Faculty of Science, Department of Mathematics.
    Krylov Sequences as a Tool for Analysing Iterated Regression Algorithms2010In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 37, no 1, p. 166-175Article in journal (Refereed)
    Abstract [en]

    We use Krylov sequences to analyse a class of regression methods based on successive identification of latent factors. Some results already proved for partial least squares regression (PLSR) are shown to hold for other methods also. We prove that the well-known peculiar pattern of alternating shrinkage and inflation of the principal components is not unique for PLSR. We also show that for any method in the class under study, the coefficient of determination is always at least as high as for principal components regression with the same number of factors.

  • 4.
    Bodnar, Taras
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Mazur, Stepan
    Parolya, Nestor
    Central limit theorems for functionals of large sample covariance matrix and mean vector in matrix-variate location mixture of normal distributions2019In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 46, no 2, p. 636-660Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the asymptotic distributions of functionals of the sample covariance matrix and the sample mean vector obtained under the assumption that the matrix of observations has a matrix-variate location mixture of normal distributions. The central limit theorem is derived for the product of the sample covariance matrix and the sample mean vector. Moreover, we consider the product of the inverse sample covariance matrix and the mean vector for which the central limit theorem is established as well. All results are obtained under the large-dimensional asymptotic regime, where the dimension p and the sample size n approach infinity such that p/n -> c is an element of [0, + infinity) when the sample covariance matrix does not need to be invertible and p/n -> c is an element of [0,1) otherwise.

  • 5.
    Britton, Tom
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Kypraios, Theodore
    O'Neill, Philip D.
    Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak2011In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 38, no 3, p. 578-599Article in journal (Refereed)
    Abstract [en]

    A stochastic epidemic model is defined in which each individual belongs to a household, a secondary grouping (typically school or workplace) and also the community as a whole. Moreover, infectious contacts take place in these three settings according to potentially different rates. For this model, we consider how different kinds of data can be used to estimate the infection rate parameters with a view to understanding what can and cannot be inferred. Among other things we find that temporal data can be of considerable inferential benefit compared with final size data, that the degree of heterogeneity in the data can have a considerable effect on inference for non-household transmission, and that inferences can be materially different from those obtained from a model with only two levels of mixing. We illustrate our findings by analysing a highly detailed dataset concerning a measles outbreak in Hagelloch, Germany.

  • 6.
    Grunewald, Maria
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Hössjer, Ola
    Stockholm University, Faculty of Science, Department of Mathematics.
    A General Statistical Framework for Multistage Designs2012In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 39, no 1, p. 131-152Article in journal (Refereed)
    Abstract [en]

    The efficiency of observational studies may be increased by applying multistage sampling designs. It is, however, not always transparent how to construct such a design to obtain increased efficiency. We here present a general statistical framework for describing and constructing multistage designs. We also provide tools for efficiency and cost-efficiency comparisons, to facilitate the choice of sampling scheme. The comparisons are based on Fisher information matrices and the results are presented in graphs, where either efficiency or cost-adjusted efficiency is plotted against a normalized measure of cost. The former curve resides in the unit square and is analogous to the receiver operating characteristic curve used for testing.

  • 7.
    Hao, Chengcheng
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    von Rosen, Dietrich
    von Rosen, Tatjana
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Local Influence Analysis in AB–BA Crossover Designs2014In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 41, no 4, p. 1153-1166Article in journal (Refereed)
    Abstract [en]

    The aim of this article is to develop methodology for detecting influential observations in crossover models with random individual effects. Various case-weighted perturbations are performed. We obtain the influence of the perturbations on each parameter estimator and on their dispersion matrices. The obtained results exhibit the possibility to obtain closed-form expressions of the influence using the residuals in mixed linear models. Some graphical tools are also presented.

  • 8.
    Lagerås, Andreas
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics. AFA Insurance, Sweden.
    Lindholm, Mathias
    Stockholm University, Faculty of Science, Department of Mathematics.
    How to ask sensitive multiple-choice questions2019In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469Article in journal (Refereed)
    Abstract [en]

    Motivated by recent failures of polling to estimate populist party support, we propose and analyze two methods for asking sensitive multiple-choice questions where the respondent retains some privacy and therefore might answer more truthfully. The first method consists of asking for the true choice along with a choice picked at random. The other method presents a list of choices and asks whether the preferred one is on the list or not. Different respondents are shown different lists. The methods are easy to explain, which makes it likely that the respondent understands how her privacy is protected and may thus entice her to participate in the survey and answer truthfully. The methods are also easy to implement and scale up.

  • 9.
    Li, Feng
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Villani, Mattias
    Efficient Bayesian Multivariate Surface Regression2013In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 40, no 4, p. 706-723Article in journal (Refereed)
    Abstract [en]

    Methods for choosing a fixed set of knot locations in additive spline models are fairly well established in the statistical literature. While most of these methods are in principle directly extendable to non-additive surface models, they are likely to be less successful in that setting because of the curse of dimensionality, especially when there are more than a couple of covariates. We propose a regression model for a multivariate Gaussian response that combines both additive splines and interactive splines, and a highly efficient MCMC algorithm that updates all the multivariate knot locations jointly. We use shrinkage priors to avoid overfitting with different estimated shrinkage factors for the additive and surface part of the model, and also different shrinkage parameters for the different response variables. This makes it possible for the model to adapt to varying degrees of nonlinearity in different parts of the data in a parsimonious way. Simulated data and an application to firm leverage data show that the approach is computationally efficient, and that allowing for freely estimated knot locations can offer a substantial improvement in out-of-sample predictive performance.

  • 10.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Flat and multimodal likelihoods and model lack of fit in curved exponential families2010In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 37, no 4, p. 632-643Article in journal (Refereed)
    Abstract [en]

    It is well known that curved exponential families can have multimodal likelihoods. We investigate the relationship between flat or multimodal likelihoods and model lack of fit, the latter measured by the score (Rao) test statistic of the curved model as embedded in the corresponding full model. When data yield a locally flat or convex likelihood (root of multiplicity >1, terrace point, saddle point, local minimum), we provide a formula for in such points, or a lower bound for it. The formula is related to the statistical curvature of the model, and it depends on the amount of Fisher information. We use three models as examples, including the Behrens-Fisher model, to see how a flat likelihood, etc. by itself can indicate a bad fit of the model. The results are related (dual) to classical results by Efron from 1978.

  • 11.
    Thorburn, Daniel
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Bondesson, Lennart
    Umeå Universitet.
    A list sequential sampling method suitable for real-time sampling2008In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 35, no 3, p. 466-483Article in journal (Refereed)
    Abstract [en]

    A flexible list sequential πps sampling method is introduced and studied. It can reproduce any given sampling design without replacement, of fixed or random sample size. The method is a splitting method and uses successive updating of inclusion probabilities. The main advantage of the method is in real-time sampling situations where it can be used as a powerful alternative to Bernoulli and Poisson sampling and can give any desired second-order inclusion probabilities and thus considerably reduce the variability of the sample size

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