Efficiency and Cramér-Rao type lnequalities for convex loss functions

A general method for obtaining inequalities of Cramér-Rao type for convex loss functions is presented. It is shown under rather weak assumptions that there are at least as many such inequalities as best unbiased estimators. More precisely, it is shown that an estimator is efficient with respect to an inequality of Cramér-Rao type if and only if it is the best in the class of unbiased estimators. Moreover, theorems of Blyth and Roberts (“Proceedings Sixth Berkeley Symposium on Math. Statist. Prob.,”) and of Blyth (Ann. Statist.2, 464–473) are extended. We make an use of methods of convex analysis and properties of convex integral functionals on Orlicz spaces.     

Stability analysis of data-driven local model networks

This article discusses stability analysis of data-driven dynamic local model networks. In contrast to traditional fuzzy modelling, the structure and complexity of such model architectures is not unique when only observed input- and output data are available for their parametrization. The present article complements the well-known trade-off between accuracy and complexity by the notion of stability. For this purpose, existing Lyapunov stability criteria for local model networks are extended by a decay rate which represents a scalar and quantitative stability measure. It allows to compare models with different degrees of complexity also in view of their stability. For some of the commonly available Lyapunov stability criteria, the individual local model transitions are crucial. Therefore, in this article, an approach is introduced to determine the actually occurring model transitions by means of the identification data. The methods presented in the article are illustrated and discussed by means of a simulation example. It is shown how model complexity and the related approximation quality can have an adverse impact on the stability and how the outcome of different Lyapunov criteria is affected by the proper determination of local model transitions.     

Gabor analysis over finite Abelian groups

Gabor frames for signals over finite Abelian groups, generated by an arbitrary lattice within the finite time–frequency plane, are the central topic of this paper. Our generic approach covers both multi-dimensional signals as well as non-separable lattices, and in fact the multi-window case as well. Our generic approach includes most of the fundamental facts about Gabor expansions of finite signals for the case of product lattices, as they have been given by Qiu, Wexler–Raz or Tolimieri–Orr, Bastiaans and Van-Leest and others. In our presentation the spreading representation of linear operators between finite-dimensional Hilbert space as well as a symplectic version of Poisson's summation formula over the finite time–frequency plane are essential ingredients. They bring us to the so-called Fundamental Identity of Gabor Analysis. In addition, we highlight projective representations of the time–frequency plane and its subgroups and explain the natural connection to twisted group algebras. In the finite-dimensional setting discussed in this paper these twisted group algebras are just matrix algebras and their structure provides the algebraic framework for the study of the deeper properties of finite-dimensional Gabor frames, independent of the structure theory theorem for finite Abelian groups.     

Intensive care unit occupancy predictions in the COVID-19 pandemic based on age-structured modelling and differential flatness

The COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. In many countries, hospitalization and in particular ICU occupancy is the primary measure for policy makers to decide on possible non-pharmaceutical interventions. In this paper a combined methodology for the prediction of COVID-19 case numbers, case-specific hospitalization and ICU admission rates as well as hospital and ICU occupancies is proposed. To this end, we employ differential flatness to provide estimates of the states of an epidemiological compartmental model and estimates of the unknown exogenous inputs driving its nonlinear dynamics. A main advantage of this method is that it requires the reported infection cases as the only data source. As vaccination rates and case-specific ICU rates are both strongly age-dependent, specifically an age-structured compartmental model is proposed to estimate and predict the spread of the epidemic across different age groups. By utilizing these predictions, case-specific hospitalization and case-specific ICU rates are subsequently estimated using deconvolution techniques. In an analysis of various countries we demonstrate how the methodology is able to produce real-time state estimates and hospital/ICU occupancy predictions for several weeks thus providing a sound basis for policy makers.

     

Solution of a roulette-type ruin problem—a correction to: a rule of thumb (not only) for gamblers

In the paper we derive a closed form formula for a probability of success in a roulette-type game. The player begins the game having j chips and plays one chip at a time. In each game, he either wins w chips with probability p or loses his chip with probability 1−p. The game terminates when the player loses all his fortune or when his capital reaches, or exceeds level C. The present solution rectifies the incorrect one, presented in Section 3 of Kozek (Stochastic Process Appl. 55 (I) (1995) 169).     

Perturbation Stability of Coherent Riesz Systems under Convolution Operators

We study the orthogonal perturbation of various coherent function systems (Gabor systems, Wilson bases, and wavelets) under convolution operators. This problem is of key relevance in the design of modulation signal sets for digital communication over time-invariant channels. Upper and lower bounds on the orthogonal perturbation are formulated in terms of spectral spread and temporal support of the prototype, and by the approximate design of worst-case convolution kernels. Among the considered bases, the Weyl–Heisenberg structure which generates Gabor systems turns out to be optimal whenever the class of convolution operators satisfies typical practical constraints.     

Long-range alignment of gold nanorods in electrospun polymer nano/microfibers

In this study, a scalable fabrication technique for controlling and maintaining the nanoscale orientation of gold nanorods (GNRs) with long-range macroscale order has been achieved through electrospinning. The volume fraction of GNRs with an average aspect ratio of 3.1 is varied from 0.006 to 0.045 in aqueous poly(ethylene oxide) solutions to generate electrospun fibers possessing different GNR concentrations and measuring 40-3000 nm in diameter. The GNRs within these fibers exhibit excellent alignment with their longitudinal axis parallel to the fiber axis n. According to microscopy analysis, the average deviant angle between the GNR axis and n increases modestly from 3.8 to 13.3° as the fiber diameter increases. Complementary electron diffraction measurements confirm preferred orientation of the {100} GNR planes. Optical absorbance spectroscopy measurements reveal that the longitudinal surface plasmon resonance bands of the aligned GNRs depend on the polarization angle and that maximum extinction occurs when the polarization is parallel to n.     

Anisotropic Thermal Processing of Polymer Nanocomposites via the Photothermal Effect of Gold Nanorods

By embedding metal nanoparticles within polymeric materials, selective thermal polymer processing can be accomplished via irradiation with light resonant with the nanoparticle surface plasmon resonance due to the photothermal effect of the nanoparticles which efficiently transforms light into heat. The wavelength and polarization sensitivity of photothermal heating from embedded gold nanorods is used to selectively process a collection of polymeric nanofibers, completely melting those fibers lying along a chosen direction while leaving the remaining material largely unheated and unaffected. Fluorescence‐based temperature and viscosity sensing was employed to confirm the presence of heating and melting in selected fibers and its absence in counter‐aligned fibers. Such tunable specificity in processing a subset of a sample, while the remainder is unchanged, cannot easily be achieved through conventional heating techniques.     

Multifunctions of faces for conditional expectations of selectors and Jensen's inequality

Let (T, , P) be a probability space, a P-complete sub-δ-algebra of and X a Banach space. Let multifunction t → Γ(t), t T, have a (X)-measurable graph and closed convex subsets of X for values. If x(t) ε Γ(t) P-a.e. and y(·) ε E_p x(·), then y(t) ε Γ(t) P-a.e. Conversely, x(t) ε F(Γ(t), y(t)) P-a.e., where F(Γ(t), y(t)) is the face of point y(t) in Γ(t). If X = , then the same holds true if Γ(t) is Borel and convex, only. These results imply, in particular, extensions of Jensen's inequality for conditional expectations of random convex functions and provide a complete characterization of the cases when the equality holds in the extended Jensen inequality.