Mostrando recursos 1 - 13 de 13

  1. The Uneven Landscape of Innovation Poles Local Embeddedness and Global Networks

    Todtling, Franz
    Series: IIR-Discussion Papers

  2. Wide Area Networks and Regional Science Recent Developments and Future Prospects

    Maier, Gunther; Wildberger, Andreas
    Series: IIR-Discussion Papers

  3. Kommunikationsnetze von Wissenschaftlern. Ergebnisse einer Fallstudie an Wiener Universitäten

    Fischer, Manfred M.; Rammer, Christian
    (no abstract available)

  4. Forecasting with Optimized Moving Local Regression

    Fedorov, Valery V.; Hackl, Peter; Müller, Werner
    This paper empirically demonstrates the relative merits of the optimal choice of the weight function in a moving local regression as suggested by Fedorov et al., (1993) over traditional weight functions which ignore the form of the local model. The discussion is based on a task that is imbedded into the smoothing methodology, namely the forecasting of business time series data with the help of a one-sided moving local regression model. (author's abstract)

  5. Optimal Design for Moving Local Regressions

    Müller, Werner
    This paper describes the so-called moving local regression, a special nonparametric statistical tool, which is firstly discussed thoroughly from analysis point of view. It avoids some drawbacks of its most serious alternatives: spline and kernel methods. The former lack computational simplicity and calculation speed, the latter may introduce high bias due to local constancy. The methods incorporation into the design framework is given, including the derivation of the necessary formulae. A Kiefer-Wolfowitz type equivalence theorem is formulated. Some geometrical examples illuminate the interrelations of the basic ingredients of the method and establish empirical relations to parametric techniques. (author's abstract)

  6. Data Augmentation and Dynamic Linear Models

    Frühwirth-Schnatter, Sylvia
    We define a subclass of dynamic linear models with unknown hyperparameters called d-inverse-gamma models. We then approximate the marginal p.d.f.s of the hyperparameter and the state vector by the data augmentation algorithm of Tanner/Wong. We prove that the regularity conditions for convergence hold. A sampling based scheme for practical implementation is discussed. Finally, we illustrate how to obtain an iterative importance sampling estimate of the model likelihood. (author's abstract)

  7. On Some Definite Integrals of a certain Class of Logarithmic Functions

    Roppert, Josef; Derflinger, Gerhard
    (no abstract available)

  8. On Zero avoiding Transition Probabilities of an r-node Tandem Queue - a Combinatorial Approach

    Böhm, Walter; Jain, J. L.; Mohanty, Sri Gopal
    In this paper we present a simple combinatorial approach for the derivation of zero avoiding transition probabilities in a Markovian r- node series Jackson network. The method we propose offers two advantages: first, it is conceptually simple because it is based on transition counts between the nodes and does not require a tensor representation of the network. Second, the method provides us with a very efficient technique for numerical computation of zero avoiding transition probabilities. (author's abstract)

  9. Moving Local Regression: The Weight Function

    Fedorov, Valery V.; Hackl, Peter; Müller, Werner
    Moving local regression is a nonparametric technique for smoothing, interpolating and forecasting by means of locally fitted regression models. The paper explores the "optimal" structure of the weight function, taking into account the location of supporting points and the suspected behaviour of the remainder term, and surveys results on choice of weight functions in traditional moving local regression approaches. (author's abstract)

  10. New generators of normal and Poisson deviates based on the transformed rejection method

    Hörmann, Wolfgang
    The transformed rejection method uses inversion to sample from the dominating density of a rejection algorithm. But in contrast to the usual method it is enough to know the inverse distribution function F^(-1)(x) of the dominating density. This idea can be applied to various continuous (e.g. normal, Cauchy and exponential) and discrete (e.g. binomial and Poisson) distributions with high acceptance probabilities. The resulting algorithms are short, simple and fast. Even more important is the fact that the quality of the method when used in combination with a linear congruential uniform generator is high compared with the quality of the ratio...

  11. A portable uniform random number generator well suited for the rejection method

    Hörmann, Wolfgang; Derflinger, Gerhard
    Up to now all known efficient portable implementations of linear congruential random number generators with modulus 2^(31)-1 are working only with multipliers which are small compared with the modulus. We show that for non-uniform distributions, the rejection method may generate random numbers of bad quality if combined with a linear congruential generator with small multiplier. Therefore a method is described that works for any multiplier smaller than 2^(30). It uses the decomposition of multiplier and seed in high order and low order bits to compute the upper and the lower half of the product. The sum of the two halfs...

  12. The transformed rejection method for generating Poisson random variables

    Hörmann, Wolfgang
    The transformed rejection method, a combination of the inversion and the rejection method, which is used to generate non-uniform random numbers from a variety of continuous distributions can be applied to discrete distributions as well. For the Poisson distribution a short and simple algorithm is obtained which is well suited for large values of the Poisson parameter $\mu$, even when $\mu$ may vary from call to call. The average number of uniform deviates required is lower than for any of the known uniformly fast algorithms. Timings for a C implementation show that the algorithm needs only half of the code...

  13. The generation of binomial random variates

    Hörmann, Wolfgang
    The transformed rejection method, a combination of inversion and rejection, which can be applied to various continuous distributions, is well suited to generate binomial random variates as well. The resulting algorithms are simple and fast, and need only a short set-up. Among the many possible variants two algorithms are described and tested: BTRS a short but nevertheless fast rejection algorithm and BTRD which is more complicated as the idea of decomposition is utilized. For BTRD the average number of uniforms required to return one binomial deviate lies between 2.5 and 1.4 which is considerably lower than for any of the...

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