Communications in Statistics: Theory and Methods

Liu, Rongfei; Wang, Dingcheng; Guo, Fenglong

doi: 10.1080/03610926.2016.1202281pmid: N/A

This article investigates the ruin probabilities of a discrete time risk model with dependent claim sizes and dependent relation between insurance risks and financial risks. The risk-free and risky investments of an insurer lead to stochastic discount factors {θn}n ⩾ 1. The claim sizes are assumed to follow a one-sided linear process with independent and identically distributed (i.i.d.) innovations {ϵn}n ⩾ 1. The i.i.d. random pairs {(ϵn, θn)}n ⩾ 1 follow a common bivariate Sarmanov-dependent distribution. When the common distribution of the innovations is heavy tailed, we establish some asymptotic estimates for the ruin probabilities of this discrete time risk model.

Park, Sangun; Noughabi, Hadi Alizadeh; Kim, Ilmun

doi: 10.1080/03610926.2017.1321767pmid: N/A

The cumulative residual Kullback–Leibler information is defined on the semi-infinite (non negative) interval. In this paper, we extend the cumulative residual Kullback–Leibler information to the whole real line and propose a general cumulative Kullback–Leibler information. We study its application to a test for normality in comparison with some competing test statistics based on the empirical distribution function including the well-known tests applied in practice like Kolmogorov–Smirnov, Cramer–von Mises, Anderson–Darling, and other existing tests.

Ghapani, F.; Babadi, B.

doi: 10.1080/03610926.2017.1321768pmid: N/A

In this paper, we introduce mixed Liu estimator (MLE) for the vector of parameters in linear measurement error models by unifying the sample and the prior information. The MLE is a generalization of the mixed estimator (ME) and Liu estimator (LE). In particular, asymptotic normality properties of the estimators are discussed, and the performance of the MLE over the LE and ME are compared based on mean squared error matrix (MSEM). Finally, a Monte Carlo simulation and a numerical example are also presented for analysis.

Yang, Jie; Yang, Weiguo; Shi, Zhiyan; Li, Yiqing; Wang, Bei; Zhang, Yue

doi: 10.1080/03610926.2017.1321770pmid: N/A

In this paper, we study the strong law of large numbers for the generalized sample relative entropy of non homogeneous Markov chains taking values from a finite state space. First, we introduce the definitions of generalized sample relative entropy and generalized sample relative entropy rate. Then, using a strong limit theorem for the delayed sums of the functions of two variables and a strong law of large numbers for non homogeneous Markov chains, we obtain the strong law of large numbers for the generalized sample relative entropy of non homogeneous Markov chains. As corollaries, we obtain some important results.

Sen, Amit

doi: 10.1080/03610926.2017.1321771pmid: N/A

We show that the Lagrange multiplier (LM) unit root test exhibits size distortions when a break in the innovation variance exists but is ignored. We develop a modified LM unit root test that is based on a generalized least-squares transformation of the original series. The asymptotic null distribution of the new modified LM unit root test is derived. Finite-sample simulation evidence shows that the modified LM unit root test maintains its size and has reasonable power against the trend stationary alternative.

Ma, Jianjing; Wang, Guojing; Yuan, George Xianzhi

doi: 10.1080/03610926.2017.1321772pmid: N/A

This article investigates the optimal reinsurance and investment problem involving a defaultable security. The insurer can purchase reinsurance and allocate his wealth among three financial securities: a money account, a stock, and a defaultable corporate bond. The objective of the insurer is to maximize the expected exponential utility of terminal wealth. Using techniques of stochastic control theory, we derive the corresponding Hamilton–Jacobi–Bellman equation and decompose the original optimization problem into a predefault case and a postdefault case. Explicit expressions for optimal strategies and the corresponding value functions are derived, and the verification theorem is given. Finally, we present numerical examples to illustrate our results.

Kayal, Tanmay; Tripathi, Yogesh Mani; Rastogi, Manoj Kumar

doi: 10.1080/03610926.2017.1322702pmid: N/A

In this paper we consider estimation of unknown parameters of an inverted exponentiated Rayleigh distribution when it is known that data are hybrid Type I censored. The maximum likelihood and Bayes estimates are derived. In sequel interval estimates are also constructed. We further consider one- and two-sample prediction of future observations and also obtain prediction intervals. The performance of proposed methods of estimation and prediction is studied using simulations and an illustrative example is discussed in support of the suggested methods.

Fazlollahtabar, Hamed; Niaki, Seyed Taghi Akhavan

doi: 10.1080/03610926.2017.1324985pmid: N/A

Current design practice is usually to produce a safety system which meets a target level of performance that is deemed acceptable by the regulators. Safety systems are designed to prevent or alleviate the consequences of potentially hazardous events. In many modern industries the failure of such systems can lead to whole system breakdown. In reliability analysis of complex systems involving multiple components, it is assumed that the components have different failure rates with certain probabilities. This leads into extensive computational efforts involved in using the commonly employed generating function (GF) and the recursive algorithm to obtain reliability of systems consisting of a large number of components. Moreover, when the system failure results in fatalities it is desirable for the system to achieve an optimal rather than adequate level of performance given the limitations placed on available resources. This paper concerns with developing a modified branching process joint with generating function to handle reliability evaluation of a multi-robot complex system. The availability of the system is modeled to compute the failure probability of the whole system as a performance measure. The results help decision-makers in maintenance departments to analyze critical components of the system in different time periods to prevent system breakdowns.

Sharma, Kush; Garg, Davinder Kumar

doi: 10.1080/03610926.2017.1324990pmid: N/A

In this paper, we have proposed a type of arrangement that we call Youden-m square and is similar to the usual Youden square but generates PBIB designs instead of BIB designs when its columns are taken as blocks. We have also discussed its construction methodologies, introduced two new m-associate class association schemes, and also constructed some series of Youden-m square type PBIB designs.

Xiaozhi, Peng; Hecheng, Wu; Ling, Ma

doi: 10.1080/03610926.2017.1324983pmid: N/A

Spatial data and non parametric methods arise frequently in studies of different areas and it is a common practice to analyze such data with semi-parametric spatial autoregressive (SPSAR) models. We propose the estimations of SPSAR models based on maximum likelihood estimation (MLE) and kernel estimation. The estimation of spatial regression coefficient ρ was done by optimizing the concentrated log-likelihood function with respect to ρ. Furthermore, under appropriate conditions, we derive the limiting distributions of our estimators for both the parametric and non parametric components in the model.