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  • 1.
    Bayram, Nuran
    et al.
    Uludag University.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Thorburn, Daniel
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Bilgel, Nazan
    Uludag University.
    Turkish immigrants in Sweden: are they integrated?2009In: The international migration review, ISSN 0197-9183, E-ISSN 1747-7379, Vol. 43, no 1, p. 90-111Article in journal (Refereed)
    Abstract [en]

    Many in Europe believe that large numbers of Turkish immigrants have failed to integrate into their host communities. How is ths situation in Sweden? We found that most of the Turkish immigrants felt themselves to be Turkish and Sweden was accepted as a foregin country. Turkish-originated media was followed frequently and strong ties with relatives in Turkey were maintained. Marriage and friendship with native Swedes were not well accepted. We conclude that despite the mulicultural aspects of Swedish integration policies, Turkish immigrants in Sweden were not well integrated and they prefer to live within the boundaries of their segregated, closed, small communities.

  • 2.
    Bruce, Daniel
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Testing for dependency of Bernoulli variables2007In: International Journal of Statistical Sciences, ISSN 1683-5603, Vol. 5Article in journal (Refereed)
    Abstract [en]

    The aim of this paper is to derive test procedures for studies where data consist of pairs of Bernoulli variables. Applications exist in, for example, ophthalmology and studies on matched pairs. Score tests and likelihood ratio tests are derived for testing the dependency between the Bernoulli variables. Multinomial logit models are used to incorporate explanatory variables. Test statistics for two particular models are thoroughly outlined. Numerical illustrations of these test statistics are presented in three examples, including one with visual impairment data.

  • 3.
    Carlson, Michael
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, HansStockholm University, Faculty of Social Sciences, Department of Statistics.Villani, MattiasStockholm University, Faculty of Social Sciences, Department of Statistics.
    Official statistics: methodology and applications in honour of Daniel Thorburn2010Collection (editor) (Other academic)
    Abstract [en]

    This book comprises articles on the methodology and applications of official statistics written by some of the best scholars and methodologists in the Nordic-Baltic region to celebrate Professor Daniel THorburn on his 65th birthday. The book's eighteen chapters span a wide spectrum of topics in official statistics, including its foundational aspects, sampling designs, survey methodology, index construction, and methods for registers and adminstrative data.

  • 4.
    Fackle-Fornius, Ellinor
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Miller, Frank
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Implementation of maximin efficient designs in dose-finding studies2015In: Pharmaceutical statistics, ISSN 1539-1604, E-ISSN 1539-1612, Vol. 14, no 1, p. 63-73Article in journal (Refereed)
    Abstract [en]

    This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four-step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose-finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs.We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose–response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design.

  • 5.
    Fackle-Fornius, Ellinor
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Optimal allocation to treatment groups under variance heterogeneity2015In: Statistica sinica, ISSN 1017-0405, E-ISSN 1996-8507, Vol. 25, no 2, p. 537-549Article in journal (Refereed)
    Abstract [en]

    The problem of allocating experimental units to treatment groups when variance heterogeneity over treatment groups is present is considered. A(A)- and D-A-optimal allocations are derived for estimation of linear combinations of treatment means. Explicit expressions for the design weights are provided for the A(A)-optimal design. The minimax strategy is introduced as an approach to handle unknown variances. Efficiencies of minimax allocations are evaluated.

  • 6.
    Fornius, Ellinor Fackle
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Using the Canonical Design Space to Obtain c-Optimal Designs for the Quadratic Logistic Model2010In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 39, no 1, p. 144-157Article in journal (Refereed)
    Abstract [en]

    c-optimal designs for estimating the model parameters of the quadratic logistic regression model are considered. The designs are constructed via the canonical design space. It is shown that the number of design points varies between 1 and 4 depending on the parameter being estimated. Furthermore, formulae for finding the design points along with the corresponding design weights are derived.

  • 7. Ghosh, Subir
    et al.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Model fitting and optimal design for a class of binary response models2016In: Journal of Statistical Planning and Inference, ISSN 0378-3758, E-ISSN 1873-1171, Vol. 179, p. 22-35Article in journal (Refereed)
    Abstract [en]

    A class of binary response models is considered for describing the data on a response variable having two possible outcomes and q explanatory variables when the odds ratios on the response are a linear function of the explanatory variables. The models provide the closed form solutions of the maximum likelihood estimating equations for the parameter estimation under a Bernoulli setup. A data example is presented to demonstrate the better goodness of fit of a model within this class in comparison with the logit, probit, and complimentary log log models. The design conditions are given and locally optimal designs are presented for some special cases under the D-, A-, and E-, optimality criterion functions. Two designs, one efficient for identifying one model and other efficient for identifying another model, are then compared for their discrimination abilities between two models even before the data collection.

  • 8.
    Magnúsdóttir, Bergrún Tinna
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Simultaneous estimation of parameters in the bivariate Emax model2015In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 34, no 28, p. 3714-3723Article in journal (Refereed)
    Abstract [en]

    In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.

  • 9.
    Miller, Frank
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Fackle-Fornius, Ellinor
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Maximin Efficient Designs for Estimating the Interesting Part of a Dose-Effect Curve2013In: 6th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2013):  , 2013Conference paper (Other academic)
    Abstract [en]

    As the costs of clinical studies increase, the demand for more efficient designs also increases. Therefore, there is a growing interest in introducing designs that optimize precision in clinical studies. Unfortunately, optimal designs generally require knowledge of unknown parameters. We consider the maximin approach to handle this problem. A maximin efficient design maximizes the efficiency when compared to a standard design, as the parameters vary in a specified subset of the parameter space. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering an Emax-sigmoid model describing a dose-response relationship and compare inferential precision with that obtained when using a uniform design. In a first approach to construct a maximin efficient design we specify a number of possible scenarios, each of which describing a possible shape of the dose-response relation. The design obtained is shown to be at least 15 percent more efficient than the uniform design. It is then shown that the obtained design is maximin efficient also for a much larger parameter set defined by parameter values between those specified by the initial scenarios.

  • 10. Özkale, M. Revan
    et al.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    The stochastic restricted ridge estimator in generalized linear models2019In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798Article in journal (Refereed)
    Abstract [en]

    Many researchers have studied restricted estimation in the context of exact and stochastic restrictions in linear regression. Some ideas in linear regression, where the ridge and restricted estimations are the well known, were carried to the generalized linear models which provide a wide range of models, including logistic regression, Poisson regression, etc. This study considers the estimation of generalized linear models under stochastic restrictions on the parameters. Furthermore, the sampling distribution of the estimators under the stochastic restriction, the compatibility test and choice of the biasing parameter are given. A real data set is analyzed and simulation studies concerning Binomial and Poisson distributions are conducted. The results show that when stochastic restrictions and ridge idea are simultaneously applied to the estimation methods, the new estimator gains efficiency in terms of having smaller variance and mean square error.

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