Bizarre tail weaponry in a transitional ankylosaur from subantarctic Chile

Armoured dinosaurs are well known for forms that evolved specialized tail weapons: paired tail spikes in stegosaurs, and heavy tail clubs in advanced ankylosaurs1. Armoured dinosaurs from southern Gondwana are rare and enigmatic, but likely include the earliest branches of Ankylosauria2-4. Here, we describe a mostly complete, semiarticulated skeleton of a small (about 2m) armoured dinosaur from the late Cretaceous of Magallanes in southernmost Chile, a region biogeographically related to West Antarctica5. Stegouros elengassen gen. et sp. nov. evolved a large tail weapon unlike any dinosaur: A flat, frond-like structure formed by 7 pairs of laterally projecting osteoderms encasing the distal half of the tail. Stegouros shows ankylosaurian cranial characters, but a largely primitive postcranial skeleton, with some stegosaur-like characters. Phylogenetic analyses placed Stegouros in Ankylosauria, and specifically related to Kunbarrasaurus from Australia6 and Antarctopelta from Antarctica7, forming a clade of Gondwanan ankylosaurs that split earliest from all other ankylosaurs. Large osteoderms and specialized tail vertebrae in Antarctopelta suggest it had a tail weapon similar to Stegouros. We propose a new clade, the Parankylosauria, to include the first ancestor of Stegouros but not Ankylosaurus, and all descendants of that ancestor.

A New One-parameter G Family of Compound Distributions: Copulas, Statistical Properties and Applications

This work introduces a new one-parameter compound G family. Relevant statistical properties are derived. The new density can be “asymmetric right skewed with one peak and a heavy tail”, “symmetric” and “left skewedwith one peak”. The new hazard function can be “upside-down”, “upside-down-constant”, “increasing”, “decreasing” and “decreasing-constant”. Many bivariate types have been also derived via different common copulas. The estimation of the model parameters is performed by maximum likelihood method. The usefulness and flexibility of the new family is illustrated by means of two real data sets.

Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting

Long-term forecasting involves predicting a horizon that is far ahead of the last observation. It is a problem of high practical relevance, for instance for companies in order to decide upon expensive long-term investments. Despite the recent progress and success of Gaussian processes (GPs) based on spectral mixture kernels, long-term forecasting remains a challenging problem for these kernels because they decay exponentially at large horizons. This is mainly due to their use of a mixture of Gaussians to model spectral densities. Characteristics of the signal important for long-term forecasting can be unravelled by investigating the distribution of the Fourier coefficients of (the training part of) the signal, which is non-smooth, heavy-tailed, sparse, and skewed. The heavy tail and skewness characteristics of such distributions in the spectral domain allow to capture long-range covariance of the signal in the time domain. Motivated by these observations, we propose to model spectral densities using a skewed Laplace spectral mixture (SLSM) due to the skewness of its peaks, sparsity, non-smoothness, and heavy tail characteristics. By applying the inverse Fourier Transform to this spectral density we obtain a new GP kernel for long-term forecasting. In addition, we adapt the lottery ticket method, originally developed to prune weights of a neural network, to GPs in order to automatically select the number of kernel components. Results of extensive experiments, including a multivariate time series, show the beneficial effect of the proposed SLSM kernel for long-term extrapolation and robustness to the choice of the number of mixture components.

Nonparametric Estimation of Extreme Quantiles with an Application to Longevity Risk

A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported. The flexible yet accurate estimation of extreme quantiles of age-at-death conditional on having survived a certain age is fundamental for evaluating the risk of lifetime insurance. Our proposal combines a parametric distributions with nonparametric sample information, leading to obtain an asymptotic unbiased estimator of extreme quantiles for alternative distributions with different right tail shape, i.e., heavy tail or exponential tail. A method for estimating the longevity risk of a continuous temporary annuity is also shown. We illustrate our proposal with an application to the official age-at-death statistics of the population in Spain.

TEMPERED PARETO-TYPE MODELLING USING WEIBULL DISTRIBUTIONS

In various applications of heavy-tail modelling, the assumed Pareto behaviour is tempered ultimately in the range of the largest data. In insurance applications, claim payments are influenced by claim management and claims may, for instance, be subject to a higher level of inspection at highest damage levels leading to weaker tails than apparent from modal claims. Generalizing earlier results of Meerschaert et al. (2012) and Raschke (2020), in this paper we consider tempering of a Pareto-type distribution with a general Weibull distribution in a peaks-over-threshold approach. This requires to modulate the tempering parameters as a function of the chosen threshold. Modelling such a tempering effect is important in order to avoid overestimation of risk measures such as the value-at-risk at high quantiles. We use a pseudo maximum likelihood approach to estimate the model parameters and consider the estimation of extreme quantiles. We derive basic asymptotic results for the estimators, give illustrations with simulation experiments and apply the developed techniques to fire and liability insurance data, providing insight into the relevance of the tempering component in heavy-tail modelling.

On the broad tails in breaking time distributions of vibrated clogging arches

Flowing grains can clog an orifice by developing arches, an undesirable event in many cases. Several strategies have been put forward to avoid this. One of them is to vibrate the system in order to undo the clogging. Nevertheless, the time taken to break an arch under a constant vibration has a distribution displaying a heavy tail. This can lead to a situation where the average breaking time is not well defined. Moreover, it has been observed in some experiments that these tails tend to flatten for very long times, exacerbating the problem. Here we will review two conceptual frameworks that have been proposed to understand the phenomenon and discuss their physical implications.