A new weighted integral goodness-of-fit statistic for exponentiality

We propose a new weighted integral goodness-of-fit statistic for exponentiality. The statistic is motivated by a characterization of the exponential distribution via the mean residual life function. Its limit null distribution is the same as that of a certain weighted integral of the squared Brownian bridge. The Laplace transform and cumulants of the latter are expressible in terms of Bessel functions.     

Smoothing of likelihood ratio statistic for equiprobable multinomial goodness-of-fit

The likelihood ratio chi-square criterion for testing goodness-of-fit in k cell multinomials is known to overestimate significance for small and moderate sample sizes (see, e.g., Larntz (1978)). Therefore, the usual chi-square approximation to the upper tail of the likelihood ratio statistic G ², is not satisfactory. Several authors have derived adjustments (e.g., Williams (1976), Smith et al. (1981), Hosmane (1987b)), so that the asymptotic mean of G ² matches the mean of the asymptotic chi-square distribution in the hope that the distribution of G ² would improve. In this paper, a new adjustment to G ² is determined on the basis of the n ^-1-order term (n being the total number) of the Edgeworth expansion of the distribution of smoothed G ². Monte Carlo results indicate that the modified G ² outperforms the unadjusted G ².     

Local influence on the goodness-of-fit test statistic in maximum likelihood factor analysis

The influence of observations on the goodness-of-fit test in maximum likelihood factor analysis is investigated by using the local influence method. Under an appropriate perturbation the test statistic forms a surface. One of main diagnostics is the maximum slope of the perturbed surface, the other is the direction vector corresponding to the curvature. These influence measures provide the information about jointly influential observations as well as individually influential observations.     

Tail fitting for truncated and non-truncated Pareto-type distributions

In extreme value analysis, natural upper bounds can appear that truncate the probability tail. At other instances ultimately at the largest data, deviations from a Pareto tail behaviour become apparent. This matter is especially important when extrapolation outside the sample is required. Given that in practice one does not always know whether the distribution is truncated or not, we consider estimators for extreme quantiles both under truncated and non-truncated Pareto-type distributions. We make use of the estimator of the tail index for the truncated Pareto distribution first proposed in Aban et al. (J. Amer. Statist. Assoc. 101(473), 270–277, 2006). We also propose a truncated Pareto QQ-plot and a formal test for truncation in order to help deciding between a truncated and a non-truncated case. In this way we enlarge the possibilities of extreme value modelling using Pareto tails, offering an alternative scenario by adding a truncation point T that is large with respect to the available data. In the mathematical modelling we hence let T→∞ at different speeds compared to the limiting fraction (k/n→0) of data used in the extreme value estimation. This work is motivated using practical examples from different fields, simulation results, and some asymptotic results.     

Asymptotic behavior of the P-value of the omega square statistic in the goodness-of-fit testing problem

The paper is devoted to the P-values of nonparametric criteria. The asymptotic lognormality of the P-value of the omega square statistic in the goodness-of-fit testing is proved.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 166, pp. 67–71, 1988.     

Local polynomial maximum likelihood estimation for Pareto-type distributions

We discuss the estimation of the tail index of a heavy-tailed distribution when covariate information is available. The approach followed here is based on the technique of local polynomial maximum likelihood estimation. The generalized Pareto distribution is fitted locally to exceedances over a high specified threshold. The method provides nonparametric estimates of the parameter functions and their derivatives up to the degree of the chosen polynomial. Consistency and asymptotic normality of the proposed estimators will be proven under suitable regularity conditions. This approach is motivated by the fact that in some applications the threshold should be allowed to change with the covariates due to significant effects on scale and location of the conditional distributions. Using the asymptotic results we are able to derive an expression for the asymptotic mean squared error, which can be used to guide the selection of the bandwidth and the threshold. The applicability of the method will be demonstrated with a few practical examples.     

A goodness-of-fit test for a polynomial errors-in-variables model

Polynomial regression models with errors in variables are considered. A goodness-of-fit test is constructed, which is based on an adjusted least-squares estimator and modifies the test introduced by Zhu et al. for a linear structural model with normal distributions. In the present paper, the distributions of errors are not necessarily normal. The proposed test is based on residuals, and it is asymptotically chi-squared under null hypothesis. We discuss the power of the test and the choice of an exponent in the exponential weight function involved in test statistics.Published in Ukrainskyi Matematychnyi Zhurnal, Vol. 56, No. 4, pp. 527–543, April, 2004.     

An adaptive estimator of the memory parameter and the goodness-of-fit test using a multidimensional increment ratio statistic

The increment ratio (IR) statistic was first defined and studied in Surgailis et al. (2007) [19] for estimating the memory parameter either of a stationary or an increment stationary Gaussian process. Here three extensions are proposed in the case of stationary processes. First, a multidimensional central limit theorem is established for a vector composed by several IR statistics. Second, a goodness-of-fit χ²-type test can be deduced from this theorem. Finally, this theorem allows to construct adaptive versions of the estimator and the test which are studied in a general semiparametric frame. The adaptive estimator of the long-memory parameter is proved to follow an oracle property. Simulations attest to the interesting accuracies and robustness of the estimator and the test, even in the non Gaussian case.     

On goodness-of-fit for accelerated life models

A goodness-of-fit test for the generalized Sedyakin's model is proposed when accelerated experiments are carried out under step-stresses. Various alternatives, such as the Cox proportional hazards model with time-varying stresses, are considered. The test power against the neighbouring alternatives is studied.     

Exact goodness-of-fit tests for censored data

The statistic introduced in Fortiana and Grané (J R Stat Soc B 65(1):115–126, 2003) is modified so that it can be used to test the goodness-of-fit of a censored sample, when the distribution function is fully specified. Exact and asymptotic distributions of three modified versions of this statistic are obtained and exact critical values are given for different sample sizes. Empirical power studies show the good performance of these statistics in detecting symmetrical alternatives.     

A new statistic for the problem

A finite -trajectory is a sequence of positive integers such that if is odd, if is even, 1$"> if and . For such a sequence we define . We prove that if is odd and . Histograms suggest that may have an interesting limiting distribution.     

次线性期望下Pareto型随机变量的大数律

本文对满足Pareto分布的随机变量建立了一些大数律,从而将经典概率空间中的相关结论推广到次线性期望空间中.基于Pareto分布,获得了一些独立随机变量序列加权和的弱大数律和强大数律.     

The laws of large numbers for Pareto-type random variables under sub-linear expectation

In this paper, some laws of large numbers are established for random variables that satisfy the Pareto distribution, so that the relevant conclusions in the traditional probability space are extended to the sub-linear expectation space. Based on the Pareto distribution, we obtain the weak law of large numbers and strong law of large numbers of the weighted sum of some independent random variable sequences.     

A generalized skewness statistic for stationary ergodic martingale differences

We present a class of generalized skewness statistics depending on a parameter β > 0 and containing the usual skewness statistic when β = 3, but providing greater flexibility for modelling and testing skewness when β ≠ 3. The statistics’ suitability for financial applications is illustrated using a large data set from the Australian share market. Data is assumed to be observations on stationary ergodicmartingale differences with possibly leptokurtic marginals, rather than independent identically distributed samples. The statistics can be studentized for use in hypothesis testing. Proof is provided of their asymptotic distributions undermild assumptions. Rates of convergence and power of the tests against skewed alternatives are assessed using simulation.     

A statistic for measuring the balance of a sample

Consider a finite population which has many auxiliary variables. A statistic, which is a function of the moments of the auxiliary variables, is proposed to measure the balance of a sample. The mean and variance of this statistic are derived.     

A multiple window scan statistic for time series models

In this article we extend the results derived for scan statistics in Wang and Glaz (2014) for independent normal observations. We investigate the performance of two approximations for the distribution of fixed window scan statistics for time series models. An R algorithm for computing multivariate normal probabilities established in Genz and Bretz (2009) can be used along with proposed approximations to implement fixed window scan statistics for ARMA models. The accuracy of these approximations is investigated via simulation. Moreover, a multiple window scan statistic is defined for detecting a local change in the mean of a Gaussian white noise component in ARMA models, when the appropriate length of the scanning window is unknown. Based on the numerical results, for power comparisons of the scan statistics, we can conclude that when the window size of a local change is unknown, the multiple window scan statistic outperforms the fixed window scan statistics.     

A χ2 goodness-of-fit test for Markov renewal processesgoodness-of-fit test for Markov renewal processes

A Markov Renewal Process (M.R.P.) is a process similar to a Markov chain, except that the time required to move from one state to another is not fixed, but is a random variable whose distribution may depend on the two states between which the transition is made. For an M.R.P. ofm (<∞) states we derive a goodness-of-fit test for a hypothetical matrix of transition probabilities. This test is similar to the test Bartlett has derived for Markov chains. We calculate the first two moments of the test statistic and modify it to fit the moments of a standard χ². Finally, we illustrate the above procedure numeerically for a particular case of a two-state M.R.P. Dwight B. Brock is mathematical statistican, Office of Statistical Methods, National Center for Health Statistics, Rockville, Maryland. A. M. Kshisagar is Associate Professor, Department of Statistics, Southern Methodist University. This research was partially supported by Office of Naval Research Contract No. N000 14-68-A-0515, and by NIH Training Grant GM-951, both with Southern Methodist University. This article is partially based on Dwight B. Brock's Ph.D. dissertation accepted by Southern Methodist University.     

A note on convex risk statistic

By weakening the comonotonic subadditivity axiom, we give the definition of the comonotonic convex risk statistic. Motivated by Ahmed et al. (2008) [1], we establish the representation results for the comonotonic convex risk statistics and the law-invariance convex risk statistics by using the convex analysis.     

The components of chi-squared statistics for goodness-of-fit tests

One obtains a formula for the transformation of a vector with a given covariance matrix. The sum of the squares of the components of the transformed vector is equal to a chi-square type quadratic form, constructed with the aid of the initial vector. Taking into account this property, one finds the components of the Pearson statistic, Pearson-Fisher statistic, etc.Translated from Veroyatnostnye Raspredeleniya i Matematicheskaya Statistika, pp. 337–350, 1986.