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This paper addresses the coherent forecasting problem for overdispersed integer-valued autoregressive (INAR) model of order one having negative binomial marginal distribution. INAR models with Poisson or geometric marginal distribution have been used by several researchers to tackle the forecasting and related issues in low count time series. However, when the process results in relatively higher counts with overdispersion, these models do not provide satisfactory fit and good forecasts. We use negative binomial INAR(1) (NBINAR(1)) model for forecasting the count time series by deriving its exact forecast distribution. Extensive simulation study has been carried out to assess the performance of the forecasts obtained using NBINAR(1) with its INAR(1) counterparts. Two real data sets have been analyzed using the proposed methodology. [ABSTRACT FROM AUTHOR]

The classical lasso estimation for sparse, high-dimensional regression models is typically biased and lacks the oracle properties. The desparsified versions of the lasso have been proposed in the literature that attempt to overcome these drawbacks. In this paper, we propose the outliers-robust version of the desparsified lasso for high dimensional generalized linear models. The robustness, consistency and high dimensional asymptotics are investigated rigorously in a general framework of M-estimation under potential model misspecification. The desparsification mechanism is subsequently utilized to construct the focused information criterion (FIC) thereby facilitating robust, focused model selection in high dimensions. The applications are demonstrated with the Poisson regression under robust quasilikelihood loss function. The empirical performance of the proposed methods is examined via simulations and a real data example. [ABSTRACT FROM AUTHOR]

This paper discusses the coherent forecasting in two types of integervalued geometric autoregressive time series models of order one, viz., Geometric Integer-valued Autoregressive (GINAR(1)) model and New Geometric Integer-valued Autoregressive (NGINAR(1)) model. GINAR(1) model uses binomial thinning for the process generation, whereas, NGINAR(1) uses negative binomial thinning. The k-step ahead conditional probability mass function and the corresponding probability generating functions are derived. It is observed that for higher order lags, the conditional mean, variance and the probability generating functions of these two processes are close to each other, whereas, for lower order lags, they differ. The coherent forecasting performance of these models is studied with the help of simulated and real data sets. [ABSTRACT FROM AUTHOR]

We propose a class of rank tests for testing the randomness of technology parameters in a stochastic frontier regression model. The asymptotic distribution of the test statistic is derived using the weak convergence results of empirical and rank processes. Since the distribution is quite complex and involves the unknown distribution of the error term, a bootstrap procedure is suggested and its validity is established. Simulation studies indicate that the test works well for most common choices of inefficiency distributions. The proposed test procedure is applied to a data set on electricity utility companies. [ABSTRACT FROM AUTHOR]

A reliability system is a configuration of several components with specific connections, rules of operation, and a component failure process. Generally, the configuration describes the features of system reliability, examples being a series system, parallel systems, or k-out-of-n system. A structure function is a mathematical formulation of this configuration. Strengthened by the above remark, this article attempts to identify inefficiencies in the working of complex systems. By inefficiency, one means any tendency or attribute that will bring down the performance of the system from the level the configuration is capable of, expected to provide, or designed for; beyond a reasonable variations due to random fluctuations.