Bandwidth selection in pre-smoothed particle filters

For the purpose of maximum likelihood estimation of static parameters, we apply a kernel smoother to the particles in the standard SIR filter for non-linear state space models with additive Gaussian observation noise. This reduces the Monte Carlo error in the estimates of both the posterior density of the states and the marginal density of the observation at each time point. We correct for variance inflation in the smoother, which together with the use of Gaussian kernels, results in a Gaussian (Kalman) update when the amount of smoothing turns to infinity. We propose and study of a criterion for choosing the optimal bandwidth h in the kernel smoother. Finally, we illustrate our approach using examples from econometrics. Our filter is shown to be highly suited for dynamic models with high signal-to-noise ratio, for which the SIR filter has problems.     

Modified Cholesky Riemann Manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets

Riemann manifold Hamiltonian Monte Carlo (RMHMC) has the potential to produce high-quality Markov chain Monte Carlo output even for very challenging target distributions. To this end, a symmetric positive definite scaling matrix for RMHMC is proposed. The scaling matrix is obtained by applying a modified Cholesky factorization to the potentially indefinite negative Hessian of the target log-density. The methodology is able to exploit the sparsity of the Hessian, stemming from conditional independence modeling assumptions, and thus admit fast implementation of RMHMC even for high-dimensional target distributions. Moreover, the methodology can exploit log-concave conditional target densities, often encountered in Bayesian hierarchical models, for faster sampling and more straightforward tuning. The proposed methodology is compared to alternatives for some challenging targets and is illustrated by applying a state-space model to real data.     

Inference about the Ratio of Two Parameters,with Application to Whale Censusing

Inference for the quotient of two parameters estimated separately may be obtained by the delta method. When the distribution of linear transformations involving the numerator and the denominator is available, more exact and elementary methods may be used. Non-Bayesian and Bayesian approaches are developed. The application of the methods to estimating the stock abundance of Northeastern Atlantic minke whales, where the ratio is a raw estimate divided by a measure of observation efficiency, is explained and discussed. The Bayesian approach allows exact inference in quite general situations using only a single, rapidly implemented, one-dimensional numerical integration. A simple analytic approximation is given for the common situation where the joint posterior distribution of the numerator and denominator can be approximated by a normal distribution that gives very little probability to negative values of the denominator. The Bayesian approach also permits the incorporation of model uncertainty (or disagreement) in a natural way, and this was the basis for the conclusions of the International Whaling Commission Scientific Committee at its 1990 meeting.     

Reflections on customers’ primary role in markets

Technology developments have transformed the business landscape by accelerating connectivity, transparency and unpredictability. We argue that the most dramatic consequence is not the possibilities created for companies but rather the challenges that emerge as a result of customer behaviour undergoing fundamental changes. Technology transformation has paved the way for empowered customers who are increasingly influencing businesses and markets, and the challenge for practitioners and researchers alike is to make sense of the role of these customers in such business environments. These developments have yielded a need to revise companies’ business models and to innovate new offerings. In scholarly research on marketing, this need has become evident and, beyond suggesting new concepts, completely new marketing management perspectives have been proposed, and each has its own core assumptions and focus. Broader views have emerged, and these stress the applicability of these perspectives not only to the top-level management of businesses but also to any non-commercial organisation.In this article, we reflect on the customer-dominant logic (CDL) of marketing, which stresses the customer's primary role in business. As such, CDL differs from approaches that focus on the provider's perspective. The CDL approach is an expression of the research ideals and goals that are based on the Nordic School of Service Management. We argue that CDL is well aligned with emerging challenges in most markets, both established and nascent, and that it is more managerially relevant than many other perspectives. This is also in line with the recent calls for a soul of relevance for European management research (Chia, 2014; Hernes, 2014).     

Fatter or fitter? On rewarding and training in a contest

Competition between heterogeneous participants leads to low-effort provision in contests. A principal can divide her fixed budget between skill-enhancing training and the contest prize. Training can reduce heterogeneity, increasing effort. It also reduces the contest prize, making effort fall. We set up an incomplete-information contest with heterogeneous players and show how this trade-off is related to the size of the budget of an effort-maximizing principal. A selection problem arises implying a cost associated with a win by the inferior player. The principal has a larger incentive to train the laggard, reducing the prize on offer.     

The precision of population projections studied by multiple prediction methods

A population projection is a prediction of a random vector variable X _T . which represents the size and age/sex distribution of the population in year T. The population is assumed to be closed and to develop according to fixed and known schedules of birth and death probabilities as a multitype branching process. The precision of the usual projection e _T (= EX _T) is studied by a family of prediction intervals of linear functions of the vector of deviations X _T — e _T , which has a preassigned probability level. This family is obtained by a multi-normal approximation and an argument similar to the one leading to Scheffé's method of multiple comparison. From the family of prediction intervals, an upper limit of the total absolute deviation Σ |X _iT ? e _it | is obtained, and the ratio of this limit to the projected total population is proposed as a measure of the relative precision of the projection. For a numerical study, Norwegian population data is used.     

Diffusion of language change: Accommodation to a moving target

The paper focuses on motivations for the spread of new features within a speech community, and on the trajectories the changes follow during diffusion. One set of data represents language use, and here focus is on two changes which have been going on in Danish over the past 40 years, one grammatical and one phonetic. The other set of data are results from a nationwide speaker evaluation experiment which tests the subconscious attitudes to different types of speech among the youth in five different places covering Denmark from east to west. Results show that changes spread centrifugally from Copenhagen, even to the extent that reversal of changes spreads from Copenhagen. Furthermore, the attitudes reflected in the speaker evaluation experiment support the theory that language change is motivated by social psychological factors. Finally, it is argued that it is worthwhile considering the possibility of media being involved in processes of linguistic change.     

Triadic closure in two-mode networks: Redefining the global and local clustering coefficients

As the vast majority of network measures are defined for one-mode networks, two-mode networks often have to be projected onto one-mode networks to be analyzed. A number of issues arise in this transformation process, especially when analyzing ties among nodes’ contacts. For example, the values attained by the global and local clustering coefficients on projected random two-mode networks deviate from the expected values in corresponding classical one-mode networks. Moreover, both the local clustering coefficient and constraint (structural holes) are inversely associated to nodes’ two-mode degree. To overcome these issues, this paper proposes redefinitions of the clustering coefficients for two-mode networks.     

On the application of improved symplectic integrators in Hamiltonian Monte Carlo

We explore the construction of new symplectic numerical integration schemes to be used in Hamiltonian Monte Carlo and study their efficiency. Integration schemes from Blanes et al., and a new scheme are considered as candidates to the commonly used leapfrog method. All integration schemes are tested within the framework of the No-U-Turn sampler (NUTS), both for a logistic regression model and a student t-model. The results show that the leapfrog method is inferior to all the new methods both in terms of asymptotic expected acceptance probability for a model problem and the efficient sample size per computing time for the realistic models.