Testing with Exponentially Tilted Empirical Likelihood

Imposing restrictions without assuming underlying distributions to modelize complex realities is a valuable methodological tool. However, if a subset of restrictions were not correctly specified, the usual test-statistics for correctly specified models tend to reject erronously a simple null hypothesis. In this setting, we may say that the model suffers from misspecification. We study the behavior of empirical phi-divergence test-statistics, introduced in Balakrishnan et al. Statistics 49:951–977 (2015), by using the exponential tilted empirical likelihood estimators of Schennach Ann Stat 35:634–672 (2007), as a good compromise between the efficiency of the significance level for small sample sizes and the robustness under misspecification.     

Comparison of Value-at-Risk models using the MCS approach

This paper compares the Value-at-Risk (VaR) forecasts delivered by alternative model specifications using the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (Econometrica 79(2):453–497, 2011). The direct VaR estimate provided by the Conditional Autoregressive Value-at-Risk (CAViaR) models of Engle and Manganelli (J Bus Econ Stat 22(4):367–381, 2004) are compared to those obtained by the popular Autoregressive Conditional Heteroskedasticity (ARCH) models of Engle (Econometrica 50(4):987–1007, 1982) and to the Generalised Autoregressive Score (GAS) models recently introduced by Creal et al. (J Appl Econom 28(5):777–795, 2013) and Harvey (Dynamic models for volatility and heavy tails: with applications to financial and economic time series. Cambridge University Press, Cambridge, 2013). The MCS procedure consists in a sequence of tests which permits to construct a set of “superior” models, where the null hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain confidence level. Our empirical results, suggest that, during the European Sovereign Debt crisis of 2009–2010, highly non-linear volatility models deliver better VaR forecasts for the European countries as opposed to other regional indexes. Model comparisons have been performed using the \(\textsf {R}\) package MCS developed by the authors and freely available at the CRAN website.     

A Trudinger–Moser Inequality on a Compact Riemannian Surface Involving Gaussian Curvature

Motivated by a recent work of Chen and Zhu (Commun Math Stat 1:369–385. 2013), we establish a Trudinger–Moser inequality on a compact Riemannian surface without boundary. The proof is based on blow-up analysis together with Carleson–Chang’s result (Bull Sci Math 110:113–127. 1986). This inequality is different from the classical one, which is due to Fontana (Comment Math Helv 68:415–454. 1993), since the Gaussian curvature is involved. As an application, we improve Chen–Zhu’s result as follows: a modified Liouville energy of conformal Riemannian metric has a uniform lower bound, provided that the Euler characteristic is nonzero and the volume of the conformal surface has a uniform positive lower bound.     

Convergence in Law for the Branching Random Walk Seen from Its Tip

Consider a critical branching random walk on the real line. In a recent paper, Aïdékon (2011) developed a powerful method to obtain the convergence in law of its minimum after a log-factor translation. By an adaptation of this method, we show that the point process formed by the branching random walk seen from the minimum converges in law to a decorated Poisson point process. This result, confirming a conjecture of Brunet and Derrida (J Stat Phys 143:420–446, 2011), can be viewed as a discrete analog of the corresponding results for the branching Brownian motion, previously established by Arguin et al. (2010, 2011) and Aïdékon et al. (2011).     

Comparative study and sensitivity analysis of skewed spatial processes

Asymmetric spatial processes arise naturally in finance, economics, hydrology and ecology. For such processes, two different classes of models are considered in this paper. One of them, proposed by Majumdar and Paul (J Comput Graph Stat 25(3):727–747, 2016), is the Double Zero Expectile Normal (DZEXPN) process and the other is a version of the “skewed normal process”, proposed by Minozzo and Ferracuti (Chil J Stat 3:157–170, 2012), with closed skew normal multivariate marginal distributions. Both spatial models have useful properties in the sense that they are ergodic and stationary. As a brief treatise to test the sensitivity and flexibility of the new proposed DZEXPN model (Majumdar and Paul in J Comput Graph Stat 25(3):727–747, 2016), in relation to other skewed spatial processes in the literature using a Bayesian methodology, our results show that by adding measurement error to the DZEXPN model, a reasonably flexible model is obtained, which is also computationally tractable than many others mentioned in the literature. Meanwhile, we develop a full-fledged Bayesian methodology for the estimation and prediction of the skew normal process proposed in Minozzo and Ferracuti (Chil J Stat 3:157–170, 2012). Specifically, a hierarchical model is used to describe the skew normal process and a computationally efficient MCMC scheme is employed to obtain samples from the posterior distributions. Under a Bayesian paradigm, we compare the performances of the aforementioned three different spatial processes and study their sensitivity and robustness based on simulated examples. We further apply them to a skewed data set on maximum annual temperature obtained from weather stations in Louisiana and Texas.     

Efficient importance sampling in low dimensions using affine arithmetic

Despite the development of sophisticated techniques such as sequential Monte Carlo (Del Moral et al. in J R Stat Soc Ser B 68(3):411–436, 2006), importance sampling (IS) remains an important Monte Carlo method for low dimensional target distributions (Chopin and Ridgway in Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation, 32:64–87, 2017). This paper describes a new technique for constructing proposal distributions for IS, using affine arithmetic (de Figueiredo and Stolfi in Numer Algorithms 37(1–4):147–158, 2004). This work builds on the Moore rejection sampler (Sainudiin in Machine interval experiments, Cornell University, Ithaca, 2005; Sainudiin and York in Algorithms Mol Biol 4(1):1, 2009) to which we provide a comparison.     

Inferences on the common mean of several normal populations under heteroscedasticity

In this paper, we consider the problem of making inferences on the common mean of several normal populations when sample sizes and population variances are possibly unequal. We are mainly concerned with testing hypothesis and constructing confidence interval for the common normal mean. Several researchers have considered this problem and many methods have been proposed based on the asymptotic or approximation results, generalized inferences, and exact pivotal methods. In addition, Chang and Pal (Comput Stat Data Anal 53:321–333, 2008) proposed a parametric bootstrap (PB) approach for this problem based on the maximum likelihood estimators. We also propose a PB approach for making inferences on the common normal mean under heteroscedasticity. The advantages of our method are: (i) it is much simpler than the PB test proposed by Chang and Pal (Comput Stat Data Anal 53:321–333, 2008) since our test statistic is not based on the maximum likelihood estimators which do not have explicit forms, (ii) inverting the acceptance region of test yields a genuine confidence interval in contrast to some exact methods such as the Fisher’s method, (iii) it works well in terms of controlling the Type I error rate for small sample sizes and the large number of populations in contrast to Chang and Pal (Comput Stat Data Anal 53:321–333, 2008) method, (iv) finally, it has higher power than recommended methods such as the Fisher’s exact method.     

Strongly consistent multivariate conditional risk measures

We consider families of strongly consistent multivariate conditional risk measures. We show that under strong consistency these families admit a decomposition into a conditional aggregation function and a univariate conditional risk measure as introduced Hoffmann et al. (Stoch Process Appl 126(7):2014–2037, 2016). Further, in analogy to the univariate case in Föllmer (Stat Risk Model 31(1):79–103, 2014), we prove that under law-invariance strong consistency implies that multivariate conditional risk measures are necessarily multivariate conditional certainty equivalents.     

Dependence structure and test of independence for some well-known bivariate distributions

In this paper, we study the dependence structure of some bivariate distribution functions based on dependence measures of Kochar and Gupta (Biometrika 74(3):664–666, 1987) and Shetty and Pandit (Stat Methods Appl 12:5–17, 2003) and then compare these measures with Spearman’s rho and Kendall’s tau. Moreover, the empirical power of the class of distribution-free tests introduced by Kochar and Gupta (1987) and Shetty and Pandit (2003) is computed based on exact and asymptotic distribution of U-statistics. Our results are obtained from simulation work in some continuous bivariate distributions for the sample of sizes \(n=6,8,15,20\) and 50. Also, we apply some examples to illustrate the results. Finally, we compare the common estimators of dependence parameter based on empirical MSE.     

On the congruence subgroup problem for branch groups

We prove here an energy estimate for the Cauchy problem for hyperbolic equations with double characteristic, which contains both effectively and non-effectively points (see L. Hörmander [3] and R. Melrose [8]) in a unique framework.     

Law of Large Numbers for Branching Symmetric Hunt Processes with Measure-Valued Branching Rates

We establish weak and strong laws of large numbers for a class of branching symmetric Hunt processes with the branching rate being a smooth measure with respect to the underlying Hunt process, and the branching mechanism being general and state dependent. Our work is motivated by recent work on the strong law of large numbers for branching symmetric Markov processes by Chen and Shiozawa (J Funct Anal 250:374–399, 2007) and for branching diffusions by Engländer et al. (Ann Inst Henri Poincaré Probab Stat 46:279–298, 2010). Our results can be applied to some interesting examples that are covered by neither of these papers.     

Impact of Dependence on Some Multivariate Risk Indicators

The minimization of some multivariate risk indicators may be used as an allocation method, as proposed in Cénac et al. (Stat Risk Model 29(1):47–71, 2012). The aim of capital allocation is to choose a point in a simplex, according to a given criterion. In Maume-Deschamps et al. (2015), it is proved that the proposed allocation technique satisfies a set of coherence axioms. In the present one, we study the properties and asymptotic behavior of the allocation for some distribution models. We also analyze the impact of the dependence structure on the allocation using some copulas.     

A new method to detect periodically correlated structure

In this paper, we introduce a new method to test whether a discrete-time periodically correlated model explains an observed time series. The proposed method is based on the estimation of the support of spectral measure. Comparisons between our procedure and the methods which were proposed by Broszkiewicz-Suwaj et al. (Phys A 336:196–205, 2004) show that our testing procedure is more powerful. We investigate the performance of the proposed method by using real and simulated datasets.     

Central Limit Theorem for Lipschitz–Killing Curvatures of Excursion Sets of Gaussian Random Fields

Our interest in this paper is to explore limit theorems for various geometric functionals of excursion sets of isotropic Gaussian random fields. In the past, asymptotics of nonlinear functionals of Gaussian random fields have been studied [see Berman (Sojourns and extremes of stochastic processes, Wadsworth & Brooks, Monterey, 1991), Kratz and León (Extremes 3(1):57–86, 2000), Kratz and León (J Theor Probab 14(3):639–672, 2001), Meshenmoser and Shashkin (Stat Probab Lett 81(6):642–646, 2011), Pham (Stoch Proc Appl 123(6):2158–2174, 2013), Spodarev (Chapter in modern stochastics and applications, volume 90 of the series Springer optimization and its applications, pp 221–241, 2013) for a sample of works in such settings], the most recent addition being (Adler and Naitzat in Stoch Proc Appl 2016; Estrade and León in Ann Probab 2016) where a central limit theorem (CLT) for Euler integral and Euler–Poincaré characteristic, respectively, of the excursions set of a Gaussian random field is proven under some conditions. In this paper, we obtain a CLT for some global geometric functionals, called the Lipschitz–Killing curvatures of excursion sets of Gaussian random fields, in an appropriate setting.     

Set-valued loss-based risk measures

In this paper, we introduce a new class of set-valued risk measures, named set-valued convex loss-based risk measures. Representation results are provided. This new class can be considered as a set-valued extension of those introduced by Cont et al. (Stat Risk Model Appl Finance Insur 30(2):133–167, 2013) and Chen et al. (Positivity, 2017). Finally, examples are also given to illustrate the set-valued convex loss-based risk measures.     

Objective Bayesian testing on the common mean of several normal distributions under divergence-based priors

This paper considers the problem of testing on the common mean of several normal distributions. We propose a solution based on a Bayesian model selection procedure in which no subjective input is considered. We construct the proper priors for testing hypotheses about the common mean based on measures of divergence between competing models. This method is called the divergence-based priors (Bayarri and García-Donato in J R Stat Soc B 70:981–1003, 2008). The behavior of the Bayes factors based DB priors is compared with the fractional Bayes factor in a simulation study and compared with the existing tests in two real examples.     

Speed of Vertex-Reinforced Jump Process on Galton–Watson Trees

We give an alternative proof of the fact that the vertex-reinforced jump process on Galton–Watson tree has a phase transition between recurrence and transience as a function of \(c\), the initial local time, see Basdevant et al. (Ann Appl Probab 22(4):1728–1743, 2012). Further, applying techniques in Aidékon (Probab Theory Relat Fields 142(3–4):525–559, 2008), we show a phase transition between positive speed and null speed for the associated discrete-time process in the transient regime.     

Stirling’s Formula for Gamma Functions,Bounds for Ratios of Gamma Functions,Beta Functions and Percentiles of a Studentized Sample Mean: A Synthesis with New Results

In the context of Stirling’s formula for gamma functions and bounds for ratios of gamma functions, this work has a threefold purpose: (1) Outline recently published literature; (2) Synthesize techniques and results from Bhattacharjee and Mukhopadhyay (Commun Stat, Theory & Methods 39:1046–1053, 2010) and Mukhopadhyay (Commun Stat, Theory & Methods 40:1283–1297, 2011) which have gone perhaps unnoticed by some recent researchers; and (3) Incorporate new results for beta functions and useful bounds for the percentiles of a Studentized sample mean obtained from a normal distribution. This synthesized review may help in gaining a wider perspective about this area.     

On the Focusing Effect of Gravitational Waves

We show that a suitable choice of the initial data from the past null infinity of Christodoulou’s work on the formation of black holes (The formation of black holes in general relativity. Monographs in Mathematics, European Mathematical Society, Zürich, 2009) will lead to the formation of a near Schwarzschild region up to the past null infinity.     

Weak Convergence of Vervaat and Vervaat Error Processes of Long-Range Dependent Sequences

Following Csörg?, Szyszkowicz and Wang (Ann. Stat. 34, 1013–1044, 2006) we consider a long range dependent linear sequence. We prove weak convergence of the uniform Vervaat and the uniform Vervaat error processes, extending their results to distributions with unbounded support and removing normality assumption.