The distribution of Pearson residuals in generalized linear models

In general, the distribution of residuals cannot be obtained explicitly. In this paper we give an asymptotic formula for the density of Pearson residuals in continuous generalized linear models corrected to order n⁻¹, where n is the sample size. We define a set of corrected Pearson residuals for these models that, to this order of approximation, have exactly the same distribution of the true Pearson residuals. An application to a real data set and simulation results for a gamma model illustrate the usefulness of our corrected Pearson residuals.     

Likelihood inference for generalized Pareto distribution

A new methodological approach that enables the use of the maximum likelihood method in the Generalized Pareto Distribution is presented. Thus several models for the same data can be compared under Akaike and Bayesian information criteria. The view is based on a detailed theoretical study of the Generalized Pareto Distribution submodels with compact support.     

The beta Burr XII distribution with application to lifetime data

For the first time, a five-parameter distribution, the so-called beta Burr XII distribution, is defined and investigated. The new distribution contains as special sub-models some well-known distributions discussed in the literature, such as the logistic, Weibull and Burr XII distributions, among several others. We derive its moment generating function. We obtain, as a special case, the moment generating function of the Burr XII distribution, which seems to be a new result. Moments, mean deviations, Bonferroni and Lorenz curves and reliability are provided. We derive two representations for the moments of the order statistics. The method of maximum likelihood and a Bayesian analysis are proposed for estimating the model parameters. The observed information matrix is obtained. For different parameter settings and sample sizes, various simulation studies are performed and compared in order to study the performance of the new distribution. An application to real data demonstrates that the new distribution can provide a better fit than other classical models. We hope that this generalization may attract wider applications in reliability, biology and lifetime data analysis.     

The bivariate generalized linear failure rate distribution and its multivariate extension

The two-parameter linear failure rate distribution has been used quite successfully to analyze lifetime data. Recently, a new three-parameter distribution, known as the generalized linear failure rate distribution, has been introduced by exponentiating the linear failure rate distribution. The generalized linear failure rate distribution is a very flexible lifetime distribution, and the probability density function of the generalized linear failure rate distribution can take different shapes. Its hazard function also can be increasing, decreasing and bathtub shaped. The main aim of this paper is to introduce a bivariate generalized linear failure rate distribution, whose marginals are generalized linear failure rate distributions. It is obtained using the same approach as was adopted to obtain the Marshall-Olkin bivariate exponential distribution. Different properties of this new distribution are established. The bivariate generalized linear failure rate distribution has five parameters and the maximum likelihood estimators are obtained using the EM algorithm. A data set is analyzed for illustrative purposes. Finally, some generalizations to the multivariate case are proposed.     

Order statistics of the generalized logistic distribution

The generalized logistic distribution function studied here, (1 + e^-χ)^-α, has parameter α 2#62;; 0 and is defined for all real χ. This distribution is useful for modeling the log odds of moderately rare events. Estimation of α by functions of order statistics is discussed. The distribution and moments of the order statistics and the sample range are also derived.     

Bayesian analysis of a generalized lognormal distribution

Many data arising in reliability engineering can be modeled by a lognormal distribution. Empirical evidences from many sources support this argument. However, sometimes the lognormal distribution does not completely satisfy the fitting expectations in real situations. This fact motivates the use of a more flexible family of distributions with both heavier and lighter tails compared to the lognormal one, which is always an advantage for robustness. A generalized form of the lognormal distribution is presented and analyzed from a Bayesian viewpoint. By using a mixture representation, inferences are performed via Gibbs sampling. Although the interest is focused on the analysis of lifetime data coming from engineering studies, the developed methodology is potentially applicable to many other contexts. A simulated and a real data set are presented to illustrate the applicability of the proposed approach.     

Characterizing the generalized lambda distribution by L-moments

The generalized lambda distribution (GLD) is a flexible four parameter distribution with many practical applications. L-moments of the GLD can be expressed in closed form and are good alternatives for the central moments. The L-moments of the GLD up to an arbitrary order are presented, and a study of L-skewness and L-kurtosis that can be achieved by the GLD is provided. The boundaries of L-skewness and L-kurtosis are derived analytically for the symmetric GLD and calculated numerically for the GLD in general. Additionally, the contours of L-skewness and L-kurtosis are presented as functions of the GLD parameters. It is found that with an exception of the smallest values of L-kurtosis, the GLD covers all possible pairs of L-skewness and L-kurtosis and often there are two or more distributions that share the same L-skewness and the same L-kurtosis. Examples that demonstrate situations where there are four GLD members with the same L-skewness and the same L-kurtosis are presented. The estimation of the GLD parameters is studied in a simulation example where method of L-moments compares favorably to more complicated estimation methods. The results increase the knowledge on the distributions that belong to the GLD family and can be utilized in model selection and estimation.     

Waiting time distribution of generalized later patterns

In this paper the concept of later waiting time distributions for patterns in multi-state trials is generalized to cover a collection of compound patterns that must all be counted pattern-specific numbers of times, and a practical method is given to compute the generalized distribution. The solution given applies to overlapping counting and two types of non-overlapping counting, and the underlying sequences are assumed to be Markovian of a general order. Patterns are allowed to be weighted so that an occurrence is counted multiple times, and patterns may be completely included in longer patterns. Probabilities are computed through an auxiliary Markov chain. As the state space associated with the auxiliary chain can be quite large if its setup is handled in a naïve fashion, an algorithm is given for generating a “minimal” state space that leaves out states that can never be reached. For the case of overlapping counting, a formula that relates probabilities for intersections of events to probabilities for unions of subsets of the events is also used, so that the distribution is also computed in terms of probabilities for competing patterns. A detailed example is given to illustrate the methodology.     

Parameter estimation of the generalized gamma distribution

This article focuses on the parameter estimation of the generalized gamma distribution. Because of many difficulties described in the literature to estimate the parameters, we propose here a new estimation method. The algorithm associated to this heuristic method is implemented in Splus. We validate the resulting routine on the particular cases of the generalized gamma distribution.     

Sampling from the generalized logarithmic series distribution

A two parameter generalized logarithmic series distribution is used to model the number of publications written by biologists. In this paper, some methods of sampling from the generalized logarithmic series distribution are presented. The inversion algorithm is compared with algorithms based on rejection and branching methods. For small values of the parameters, the inversion algorithm seems to be faster than other algorithms. The paper recommends a modified algorithm based on the inversion and branching methods.     

Random sampling from the generalized gamma distribution

This paper presents a simple and easy to implement algorithm for sampling from the generalized four parameter gamma distribution proposed by Stacy. The proposed method is based on a generalization of Von Neumann's rejection method where the first stage sampling is done from the log logistic distribution. The proposed method is simple, easy to implement and faster than the traditional methods for generating generalized gamma variates.     

The DCM generalized inverse: efficient body-wrench distribution in multi-contact balance control

A computationally efficient solution of the body- wrench distribution problem for bipeds and multi-legged robots is introduced. The method is based on a weighted generalized inverse, the weights being determined from relationships pertinent to the divergent component of motion (DCM), base-of-support (BoS) geometry, friction constraints and center of pressure allocation. The user (or the robot) specifies appropriate weights only indirectly, by setting the desired contact transition boundaries within the net BoS. It is shown that the proposed weighted generalized inverse ensures body- wrench distribution in a way consistent with both the static and dynamic states. The dependency on the DCM yields an important advantage when the method is applied to reactive balance control in response to unknown disturbances. An admittance-type stabilizer is obtained by setting the reference DCM at the current center of mass position. This stabilizer does not require reference values for the centers of pressures and the contact wrenches. The method is implemented with a whole-body, torque-based balance controller. Its performance is examined through simulations with planar and non-coplanar contacts, during proactive and reactive tasks.     

Estimating the parameters of a generalized lambda distribution

The method of moments is a popular technique for estimating the parameters of a generalized lambda distribution (GLD), but published results suggest that the percentile method gives superior results. However, the percentile method cannot be implemented in an automatic fashion, and automatic methods, like the starship method, can lead to prohibitive execution time with large sample sizes. A new estimation method is proposed that is automatic (it does not require the use of special tables or graphs), and it reduces the computational time. Based partly on the usual percentile method, this new method also requires choosing which quantile u to use when fitting a GLD to data. The choice for u is studied and it is found that the best choice depends on the final goal of the modeling process. The sampling distribution of the new estimator is studied and compared to the sampling distribution of estimators that have been proposed. Naturally, all estimators are biased and here it is found that the bias becomes negligible with sample sizes n?2×10³. The .025 and .975 quantiles of the sampling distribution are investigated, and the difference between these quantiles is found to decrease proportionally to . The same results hold for the moment and percentile estimates. Finally, the influence of the sample size is studied when a normal distribution is modeled by a GLD. Both bounded and unbounded GLDs are used and the bounded GLD turns out to be the most accurate. Indeed it is shown that, up to n=10⁶, bounded GLD modeling cannot be rejected by usual goodness-of-fit tests.     

L-moments and TL-moments of the generalized lambda distribution

The 4-parameter generalized lambda distribution (GLD) is a flexible distribution capable of mimicking the shapes of many distributions and data samples including those with heavy tails. The method of L-moments and the recently developed method of trimmed L-moments (TL-moments) are attractive techniques for parameter estimation for heavy-tailed distributions for which the L- and TL-moments have been defined. Analytical solutions for the first five L- and TL-moments in terms of GLD parameters are derived. Unfortunately, numerical methods are needed to compute the parameters from the L- or TL-moments. Algorithms are suggested for parameter estimation. Application of the GLD using both L- and TL-moment parameter estimates from example data is demonstrated, and comparison of the L-moment fit of the 4-parameter kappa distribution is made. A small simulation study of the 98th percentile (far-right tail) is conducted for a heavy-tail GLD with high-outlier contamination. The simulations show, with respect to estimation of the 98th-percent quantile, that TL-moments are less biased (more robost) in the presence of high-outlier contamination. However, the robustness comes at the expense of considerably more sampling variability.     

Lifetime analysis based on the generalized Birnbaum-Saunders distribution

In this paper, we consider a family of generalized Birnbaum-Saunders distributions and present a lifetime analysis based mainly on the hazard function of this model. In addition, we carry out maximum likelihood estimation by using an iterative algorithm, which produces robust estimates. Asymptotic inference is also presented. Next, the quality of the estimation method is examined by means of Monte Carlo simulations. We then provide a practical example to illustrate the obtained results. From this example and based on goodness-of-fit methods, we show that the GBS distribution results in a more appropriate model for modeling fatigue data than other models commonly used to model this type of data. Finally, we estimate the hazard function and the critical point of this function.     

Testing for the generalized normal-Laplace distribution with applications

The generalized normal-Laplace distribution is a useful law for modelling asymmetric data exhibiting excess kurtosis. Goodness-of-fit tests for this distribution are constructed which utilize the corresponding moment generating function, and its empirical counterpart. The consistency and other properties of the test are investigated under general assumptions, and the proposed procedure is applied, following a non-trivial estimation step, to test the fit of some financial data.     

A generalized modified Weibull distribution for lifetime modeling

A four parameter generalization of the Weibull distribution capable of modeling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone as well as non-monotone failure rates, which are quite common in lifetime problems and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the Weibull, extreme value, exponentiated Weibull, generalized Rayleigh and modified Weibull distributions, among others. We derive two infinite sum representations for its moments. The density of the order statistics is obtained. The method of maximum likelihood is used for estimating the model parameters. Also, the observed information matrix is obtained. Two applications are presented to illustrate the proposed distribution.     

A generalized distribution model for random recursive trees

 A random tree T _n of order n is constructed by choosing in a random tree T _n-1 of order n−1 a vertex at random and connecting it to a new vertex labeled n. In the usual constraint we assume that the n−1 vertices of T _n-1 are equally likely to be chosen. We introduce and research a more general case in which a distribution of choosing vertices is defined by a sequence α₁, α₂, …. Received: 14 February 1995/2 January 1996     

Inference for Weibull distribution under generalized order statistics

Based on generalized order statistics from Weibull distribution the approach of Bayesian and non-Bayesian estimation are discussed. We present a simple and efficient simulational algorithm for generating a generalized order statistics sample from any continuous distribution. Specializations to Bayesian and non-Bayesian estimators, some lifetime parameters and confidence intervals of progressive II censoring and record values are obtained and compared with the existing results. Two examples are given to illustrate the proposed estimators and the simulation algorithm.