Some recent results on the distributions of stopping times of compound Poisson processes with linear boundaries

A review of recent results is given, for the distribution of stopping times defined on compound Poisson processes and linear boundaries. Generalization of the results of Zacks (Commun. Statist. Stochastic Models 7 (1991) 233) is given for discrete compound Poisson processes. The main approach in the reviewed papers is that of sample path analysis, which leads to renewal type equations. Explicit solutions are given.     

SHIFTED HILL'S ESTIMATOR FOR HEAVY TAILS

Hill's estimator is a popular method for estimating the thickness of heavy tails. In this paper we modify Hill's estimator to make it shift-invariant as well as scale-invariant. The resulting shifted Hill's estimator is a more robust method of estimating tail thickness.

     

Hill's estimator under weak dependence

In this article, we investigate the asymptotic normality of the Hill's estimator of the tail index parameter, when the observations are weakly dependent in the sense of Doukhan and Louhichi (1999 Doukhan, P., Louhichi, S. (1999). A new weak dependence condition and applications to moment inequalities. Stochastic Process. Appl. 84:313–342.[Crossref], [Web of Science ®] , [Google Scholar]) and are drawn from a strictly linear process. We show that the previous result on Hill estimator obtained by Rootzen et al. (1990 Rootzen, H., Leadbetter, M., De Haan, L. (1990). Tail and quantile estimation for strongly mixing stationary sequences. Technical report. No. 292, Center for Stochastic Processes, Department of Statistics, University of North Carolina, Chapel Hill. [Google Scholar]) and Resnick and Starica (1997 Resnick, S., Starica, C. (1997). Asymptotic behavior of Hill's estimator for autoregressive data. Commun. Statistics-stochastic Models 13:703–723.[Taylor &; Francis Online] , [Google Scholar]) for strong mixing can be extended to weak dependence.     

A new estimator of entropy and its application in testing normality

In this paper, we introduce a new estimator of entropy of a continuous random variable. We compare the proposed estimator with the existing estimators, namely, Vasicek [A test for normality based on sample entropy, J. Roy. Statist. Soc. Ser. B 38 (1976), pp. 54–59], van Es [Estimating functionals related to a density by class of statistics based on spacings, Scand. J. Statist. 19 (1992), pp. 61–72], Correa [A new estimator of entropy, Commun. Statist. Theory and Methods 24 (1995), pp. 2439–2449] and Wieczorkowski-Grzegorewski [Entropy estimators improvements and comparisons, Commun. Statist. Simulation and Computation 28 (1999), pp. 541–567]. We next introduce a new test for normality. By simulation, the powers of the proposed test under various alternatives are compared with normality tests proposed by Vasicek (1976) and Esteban et al. [Monte Carlo comparison of four normality tests using different entropy estimates, Commun. Statist.–Simulation and Computation 30(4) (2001), pp. 761–785].     

Rao and Wu's re-scaling bootstrap modified to achieve extended coverages

Horvitz and Thompson's (HT) [1952. A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47, 663–685] well-known unbiased estimator for a finite population total admits an unbiased estimator for its variance as given by [Yates and Grundy, 1953. Selection without replacement from within strata with probability proportional to size. J. Roy. Statist. Soc. B 15, 253–261], provided the parent sampling design involves a constant number of distinct units in every sample to be chosen. If the design, in addition, ensures uniform non-negativity of this variance estimator, Rao and Wu [1988. Resampling inference with complex survey data. J. Amer. Statist. Assoc. 83, 231–241] have given their re-scaling bootstrap technique to construct confidence interval and to estimate mean square error for non-linear functions of finite population totals of several real variables. Horvitz and Thompson's estimators (HTE) are used to estimate the finite population totals. Since they need to equate the bootstrap variance of the bootstrap estimator to the Yates and Grundy's estimator (YGE) for the variance of the HTE in case of a single variable, i.e., in the linear case the YG variance estimator is required to be positive for the sample usually drawn.     

Generalized least-squares estimators for the thickness of heavy tails

For a probability distribution with power law tails, a log–log transformation makes the tails of the empirical distribution function resemble a straight line, leading to a least-squares estimate of the tail thickness. Taking into account the mean and covariance structure of the extreme order statistics leads to improved tail estimators, and a surprising connection with Hill's estimator.     

Statistical learning theory for fitting multimodal distribution to rainfall data: an application

The promising methodology of the “Statistical Learning Theory” for the estimation of multimodal distribution is thoroughly studied. The “tail” is estimated through Hill's, UH and moment methods. The threshold value is determined by nonparametric bootstrap and the minimum mean square error criterion. Further, the “body” is estimated by the nonparametric structural risk minimization method of the empirical distribution function under the regression set-up. As an illustration, rainfall data for the meteorological subdivision of Orissa, India during the period 1871–2006 are used. It is shown that Hill's method has performed the best for tail density. Finally, the combined estimated “body” and “tail” of the multimodal distribution is shown to capture the multimodality present in the data.     

Goodness-of-fit test based on correcting moments of modified entropy estimator

In this paper, we first propose a new estimator of entropy for continuous random variables. Our estimator is obtained by correcting the coefficients of Vasicek's [A test for normality based on sample entropy, J. R. Statist. Soc. Ser. B 38 (1976), pp. 54–59] entropy estimator. We prove the consistency of our estimator. Monte Carlo studies show that our estimator is better than the entropy estimators proposed by Vasicek, Ebrahimi et al. [Two measures of sample entropy, Stat. Probab. Lett. 20 (1994), pp. 225–234] and Correa [A new estimator of entropy, Commun. Stat. Theory Methods 24 (1995), pp. 2439–2449] in terms of root mean square error. We then derive the non-parametric distribution function corresponding to our proposed entropy estimator as a piece-wise uniform distribution. We also introduce goodness-of-fit tests for testing exponentiality and normality based on the said distribution and compare its performance with their leading competitors.     

Sequential unbiased estimation of parameters in a two-way contingency table

Classical analysis of contingency tables employs (i) fixed sample sizes and (ii) the maximum likelihood and weighted least squares approach to parameter estimation. It is well-known, however, that certain important parameters, such as the main effect and interaction parameters, can neverbe estimated unbiasedly when the sample size is fixed a priori We introduce a sequential unbiased estimator for the cell probabilities subject to log linear constraints. As a simple consequence, we show how parameters such as those mentioned above may. be estimated unbiasedly. Our unbiased estimator for the vector of cell probabilities is shown to be consistent in the sense of Wolfowitz (Ann. Math. Statist. (1947) 18). We give a sufficient condition on a multinomial stopping rule for the corresponding sufficient statistic to be complete. When this condition holds, we have a unique minimum variance unbiased estimator for the cell probabilities.     

OPTIMAL SEMIPARAMETRIC INFERENCE FOR THE TAIL INDEX BASED ON RATIOS OF THE LARGEST EXTREMES

The k largest order statistics in a random sample from a common heavy‐tailed parent distribution with a regularly varying tail can be characterized as Fréchet extremes. This paper establishes that consecutive ratios of such Fréchet extremes are mutually independent and distributed as functions of beta random variables. The maximum likelihood estimator of the tail index based on these ratios is derived, and the exact distribution of the maximum likelihood estimator is determined for fixed k, and the asymptotic distribution as k →∞ . Inferential procedures based upon the maximum likelihood estimator are shown to be optimal. The Fréchet extremes are not directly observable, but a feasible version of the maximum likelihood estimator is equivalent to Hill's statistic. A simple diagnostic is presented that can be used to decide on the largest value of k for which an assumption of Fréchet extremes is sustainable. The results are illustrated using data on commercial insurance claims arising from fires and explosions, and from hurricanes.     

A functional law of the iterated logarithm for kernel-type estimators of the tail index

Under von Mises's condition, the kernel tail index estimators of Csörgő et al. (Ann. Statist. 13 (1985) 1050) have been generalized by Groeneboon et al. (Ann. Statist. 31 (2003) 1956) for all real tail indices. Weak consistency and asymptotic normality of their kernel estimators have been established. In this paper, we present a characterization of the almost sure behavior of these estimators and we show their strong consistency.     

On the entropy estimators

The paper introduces an estimator of the entropy of a continuous random variable. The estimator is obtained by modifying the estimator proposed by Ebrahimi et al. [Two measures of sample entropy, Statist. Probab. Lett. 20 (1994), pp. 225–234]. The consistency of the estimator is proved and comparisons are made with Vasicek's estimator [A test for normality based on sample entropy, J. R. Stat. Soc. Ser. B 38 (1976), pp. 54–59], van Es estimator [Estimating functionals related to a density by class of statistics based on spacings, Scand. J. Statist. 19 (1992), pp. 61–72], Ebrahimi et al. estimator and Correa estimator [A new estimator of entropy, Comm. Statist. Theory Methods 24 (1995), pp. 2439–2449]. The results indicate that the proposed estimator has smaller mean-squared error than above estimators. A real example is presented and analysed.     

Stability under contamination of robust regression estimators based on differences of residuals

A reasonable approach to robust regression estimation is minimizing a robust scale estimator of the pairwise differences of residuals. We introduce a large class of estimators based on this strategy extending ideas of Yohai and Zamar (Am. Statist. (1993) 1824–1842) and Croux et al. (J. Am. Statist. Assoc. (1994) 1271–1281). The asymptotic robustness properties of the estimators in this class are addressed using the maxbias curve. We provide a general principle to compute this curve and present explicit formulae for several particular cases including generalized versions of S-, R- and τ-estimators. Finally, the most stable estimator in the class, that is, the estimator with the minimum maxbias curve, is shown to be the set of coefficients that minimizes an appropriate quantile of the distribution of the absolute pairwise differences of residuals.     

Subsampling,symmetrization, and robust interpolation

The recently developed subsampling methodology has been shown to be valid for the construction of large-sample confidence regions for a general unknown parameter 9 under very minimal conditions. Nevertheless, in some specific cases—e.g. in the case of the sample mean of i.i.d. data—it has been noted that the subsampling distribution estimator underperforms as compared to alternative estimators such as the bootstrap or the asymptotic normal distribution (with estimated variance). In the present report we introduce a (partially) symmetrized.     

Moment-based tail index estimation

A general method of tail index estimation for heavy-tailed time series, based on examining the growth rate of the logged sample second moment of the data was proposed and studied in Meerschaert and Scheffler (1998. A simple robust estimator for the thickness of heavy tails. J. Statist. Plann. Inference 71, 19–34) as well as Politis (2002. A new approach on estimation of the tail index. C. R. Acad. Sci. Paris, Ser. I 335, 279–282). To improve upon the basic estimator, we introduce a scale-invariant estimator that is computed over subsets of the whole data set. We show that the new estimator, under some stronger conditions on the data, has a polynomial rate of consistency for the tail index. Empirical studies explore how the new method compares with the Hill, Pickands, and DEdH estimators.     

Estimating the common hazard rate of two exponential distributions with ordered location parameters

This paper is concerned with estimating the common hazard rate of two exponential distributions with unknown and ordered location parameters under a general class of bowl-shaped scale invariant loss functions. The inadmissibility of the best affine equivariant estimator is established by deriving an improved estimator. Another estimator is obtained which improves upon the best affine equivariant estimator. A class of improving estimators is derived using the integral expression of risk difference approach of Kubokawa [A unified approach to improving equivariant estimators. Ann Statist. 1994;22(1):290–299]. These results are applied to specific loss functions. It is further shown that these estimators can be derived for four important sampling schemes: (i) complete and i.i.d. sample, (ii) record values, (iii) type-II censoring, and (iv) progressive Type-II censoring. A simulation study is carried out for numerically comparing the risk performance of these proposed estimators.     

A mixed randomized response model

To implement the privacy problem with the Moors (J. Amer. Statist. Assoc. 66 (1971) 627) model, Mangat et al. (Commun. Statist. Theory Methods 26(3) (1997) 243) and Singh et al. (J. Statist. Plann. Inference 83 (2000) 243) presented several strategies as an alternative to Moors model, but their models may lose a large portion of data information and require a high cost to obtain confidentiality of the respondents. Our proposed model has the advantage of simplicity over the previous models while protecting confidentiality. We extend the proposed model to stratified sampling.     

Confidence intervals for endpoints of a c.d.f. via bootstrap

We propose methods of constructing confidence intervals for endpoints of a distribution. Under a mild condition on the tail of distribution, asymptotically correct confidence intervals are derived by bootstrapping Weissman's (Comm. Statist. Theory Method A 10 (1981) 549–557) statistics. It is also shown that a modification of this method works for type II censored data.     

On the information content of partial observations

The modified zero order approach to estimating coefficients in the face of missing observations treats them as parameters to be estimated simultaneously with the missing observations. The paper then investigates (in the context of Han's generalized regression model)(i) when parameter estimators don't vary between using the partial data points and using only the complete ones (the informationless result), and (ii) large sample properties of the modified zero order estimator. It's found the sequential cut property is crucial to the informationless result for coefficient estimators; consistency of the modified zero order estimator depends on the percentage of observations with missing elements for large sample sizes or the sequential cut property.     

Maximum likelihood revisited under a semi-parametric context - estimation of the tail index

In this paper, and in a context of regularly varying tails, we study computationally the classical Maximum Likelihood (ML) estimator based on the Paretian behaviour of the excesses over a high threshold, denoted PML-estimator, a type II Censoring estimator based specifically on a Fréchet parent, denoted CENS-estimator, and two ML estimators based on the scaled log-spacings, and denoted SLS-estimators. These estimators are considered under a semi-parametric set-up, and compared with the classical Hill estimator and a Generalized Jackknife (GJ) estimator, which has essentially in mind a reduction of the bias of Hill's estimator.