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In this paper, we propose a new bootstrap algorithm to obtain prediction intervals for generalized autoregressive conditionally heteroscedastic (GARCH(1,1)) process which can be applied to construct prediction intervals for future returns and volatilities. The advantages of the proposed method are twofold: it (a) often exhibits improved performance and (b) is computationally more efficient compared...
Portmanteau tests are some of the most commonly used statistical methods for model diagnostics. They can be applied in model checking either in the time series or in the regression context. The present paper proposes a portmanteau-type test, based on a sort of likelihood ratio statistic, useful to test general parametric hypotheses inherent to statistical models, which includes the classical portmanteau...
The over all regression function in a semi-parametric model involves a partly specified regression function in some primary covariates and a non-parametric function in some other secondary covariates. This type of semi-parametric models in a longitudinal setup has recently been discussed extensively both for repeated Poisson and negative binomial count data. However, when it is appropriate to interpret...
This note concerns a family of distributions on the unit sphere obtained by inverse stereographic projection of elliptical distributions. We give some properties of these distributions with emphasis on the study of unimodality. This construction encompasses many known families of distributions on the unit sphere. Finally we show that it is possible to define new families of unimodal distributions...
Consider the complete graph Kn on n vertices where each edge e is independently open with probability pn(e) or closed otherwise. The edge probabilities are not necessarily same but are close to some positive constant C. The resulting random graph G is in general inhomogenous and we use a tree counting argument to establish phase transition in the following sense: For C < 1, all components of G...
Asymptotic normality of intermediate order statistics taken from univariate iid random variables is well-known. We generalize this result to random vectors in arbitrary dimension, where the order statistics are taken componentwise.
In recent times, the beta process has been widely used as a nonparametric prior for different models in machine learning, including latent feature models. In this paper, we prove the asymptotic consistency of the finite dimensional approximation of the beta process due to Paisley and Carin (2009). In particular, we show that this finite approximation converges in distribution to the Ferguson and Klass...
Dimension reduction procedures have received increasing consideration over the past decades. Despite this attention, the effect of data contamination or outlying data points in dimension reduction is, however, not well understood, and is compounded by the issue that outliers can be difficult to classify in the presence of many variables. This paper formally investigates the influence of data contamination...
Assuming squared error loss, we show that finding unbiased estimators and Bayes estimators can be treated as using a pair of linear operators that operate between two Hilbert spaces. We note that these integral operators are adjoint and then investigate some consequences of this fact. An extension to loss functions that can be defined via an inner product is also presented.
Consider a helix in three-dimensional space along which a sequence of equally spaced points is observed, subject to statistical noise. For data coming from a single helix, a two-stage algorithm based on a profile likelihood is developed to compute the maximum likelihood estimate of the helix parameters. Statistical properties of the estimator are studied and comparisons are made to other estimators...
We consider the problem of simultaneous estimation of two population means when one suspects that the two means are nearly equal. It is shown that the hierarchical empirical Bayes estimators which shrink the sample means towards the suspected hypothesis dominate the sample mean vectors in simultaneous estimation under the divergence loss function.
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However, MCMC algorithms tend to be computationally demanding, and are particularly slow for large datasets. Data subsampling has recently been suggested as a way to make MCMC methods scalable...
This paper deals with uncertainty quantification (UQ) for a class of robust estimators of population parameters of a stationary, multivariate random field that is observed at a finite number of locations s1,…, sn, generated by a stochastic design. The class of robust estimators considered here is given by the so-called M-estimators that in particular include robust estimators of location, scale, linear...
We consider the problem of Bayesian discriminant analysis using a high dimensional predictor. In this setting, the underlying precision matrices can be estimated with reasonable accuracy only if some appropriate additional structure like sparsity is assumed. We induce a prior on the precision matrix through a sparse prior on its Cholesky decomposition. For computational ease, we use shrinkage priors...
The Lorenz curve is a much used instrument in economic analysis. It is typically used for measuring inequality and concentration. In insurance, it is used to compare the riskiness of portfolios, to order reinsurance contracts and to summarize relativity scores (see Frees et al. J. Am. Statist. Assoc.106, 1085–1098, 2011; J. Risk Insur.81, 335–366, 2014 and Samanthi et al. Insur. Math. Econ.68, 84–91,...
The paper concerns a random property T of a manufactured product that must with high probability e.g. P* = 95% exceed a specified quantity ηa called the characteristic value (CV). However the product comes from any one of K different subpopulations that may represent such things as manufacturers, regions or countries; the distribution of T will generally differ from one subpopulation to another and...
This paper suggests a multivariate asymmetric kernel density estimation using a multivariate weighted log-normal (LN) kernel for non-negative multivariate data. Asymptotic properties of the multivariate weighted LN kernel density estimator are studied. Simulation studies are also conducted in the bivariate situation.
Consider a population of individuals belonging to an infinity number of types, and assume that type proportions follow the Poisson-Dirichlet distribution with parameter α ∈ [0,1) and 𝜃 > −α. Given a sample of size n from the population, two important statistics are the number Kn of different types in the sample, and the number Ml,n of different types with frequency l in the sample. We establish...
In the general risk model (or the Sparre-Andersen model), it is well-known that the following assertion holds: if the claim size is exponentially distributed then the non-ruin probability distribution is a mixture of exponential distributions. In this paper, under some general conditions, we prove that the converse statement of the previous assertion is also true. Besides, we define a new non-ruin...
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