Resampling Procedures with Empirical Beta Copulas

The empirical beta copula is a simple but effective smoother of the empirical copula. Because it is a genuine copula, from which, moreover, it is particularly easy to sample, it is reasonable to expect that resampling procedures based on the empirical beta copula are expedient and accurate. In this paper, after reviewing the literature on some bootstrap approximations for the empirical copula process, we first show the asymptotic equivalence of several bootstrapped processes related to the empirical copula and empirical beta copula. Then we investigate the finite-sample properties of resampling schemes based on the empirical (beta) copula by Monte Carlo simulation. More specifically, we consider interval estimation for some functionals such as rank correlation coefficients and dependence parameters of several well-known families of copulas, constructing confidence intervals by several methods and comparing their accuracy and efficiency. We also compute the actual size and power of symmetry tests based on several resampling schemes for the empirical copula and empirical beta copula.Comment: 22 pages, 8 table

The Empirical Beta Copula

Given a sample from a multivariate distribution $F$, the uniform random variates generated independently and rearranged in the order specified by the componentwise ranks of the original sample look like a sample from the copula of $F$. This idea can be regarded as a variant on Baker's [J. Multivariate Anal. 99 (2008) 2312--2327] copula construction and leads to the definition of the empirical beta copula. The latter turns out to be a particular case of the empirical Bernstein copula, the degrees of all Bernstein polynomials being equal to the sample size. Necessary and sufficient conditions are given for a Bernstein polynomial to be a copula. These imply that the empirical beta copula is a genuine copula. Furthermore, the empirical process based on the empirical Bernstein copula is shown to be asymptotically the same as the ordinary empirical copula process under assumptions which are significantly weaker than those given in Janssen, Swanepoel and Veraverbeke [J. Stat. Plan. Infer. 142 (2012) 1189--1197]. A Monte Carlo simulation study shows that the empirical beta copula outperforms the empirical copula and the empirical checkerboard copula in terms of both bias and variance. Compared with the empirical Bernstein copula with the smoothing rate suggested by Janssen et al., its finite-sample performance is still significantly better in several cases, especially in terms of bias.Comment: 23 pages, 3 figure

Weak convergence and the prediction process

We first consider convergence in law of measurable processes with a general parameter set and a state space. To this end, we need to investigate topological properties of the space of measurable functions which is the paths space of measurable processes. Also a characterization of compact sets in the space is derived and some functionals on the space are discussed.We then proceed to prove some properties of probability measures on the space of measurable functions. After investigating conditions on function spaces which guarantee that weak convergence may be proved by establishing finite-dimensional convergence and tightness, we prove necessary and sufficient conditions for convergence in law of measurable processes. These results are then applied to convergence of the prediction process; a special case where the given process is Markov is studied in detail.We also derive two results on the prediction process which is not in the field of weak convergence. One is on the asymptotic behavior of the prediction process under absolutely continuity, and the other is an example which shows that the prediction process may lost Markov property if one uses a larger filtration than the natural one.U of I OnlyETDs are only available to UIUC Users without author permissio

A Limit Theorem for the Prediction Process under Absolute Continuity

Consider a stochastic process with two probability laws, one of which is absolutely continuous with respect to the other. Under each law, we look at a process consisting of the conditional distributions of the future given the past. Blackwell and Dubins (1962) showed in discrete case that those conditional distributions merge as we observe more and more; more precisely, the total variation distance between them converges to 0 a.s. In this paper we prove its extension to continuous time case using the prediction process of F. B. Knight.

A Note on the Prediction Process

In this note, we give an example to show that the prediction process may lost Markov property if the future of the process which generates the known past is not included in the future to be predicted.

Convergence in Law of Measurable Processes with Applications to the Prediction Process

We study convergence in law of measurable processes with a general state space and a parameter set. The space of measurable functions are first investigated and we examine properties of probability measure on the space. A necessary and sufficient condition for convergence in law of measurable processes is obtained. These general results are applied to the prediction process, and we show that convergence of the prediction processes implies that of given processes. We also find a simple condition for convergence of the prediction processes when given processes are Markovian.

Two Transformation Models and Rank Estimation

In the first half of the paper, we shall investigate the relation of two models both of which have been called a transformation model. One model is in terms of distribution functions and the other is in terms of random variables. We shall show that the former class is larger than the latter and we give an explicit relation between these models. The second half deals with estimation procedures for the regression parameters in the transformation model in terms of distribution functions. After reviewing and extending previously proposed estimators for the model, we derive a new estimator based on ranks. Monte Carlo simulation is performed to compare the empirical properties of several estimators for the Cox model, which is a particular case of our transformation model.