Spatio-temporal Trend Analysis of Spring Arrival Data for Migratory Birds

There is increasing interest in spatio-temporal analysis of environmental and ecological responses to changes in the climate due to the recent concerns about climate change. In this work, we propose a spatio-temporal modeling framework for analyzing environmental and ecological data while accounting for spatial and temporal structure, as well as climate effects. As an example, we consider data on bird migration in the United States and analyze the spring arrival dates of Purple Martins between historical data (1905–1940) from the North American Bird Phenology Program and recent data (2001–2010) from the Purple Martin Conservation Association. The proposed approach allows researchers to compare mean arrival dates while accounting for spatial and temporal variability. Our results for Purple Martins showed statistically significant earlier spring arrivals in parts of United States over the recent years. The proposed approach provides a useful tool for statistical analysis of spatio-temporal data related to studies of climate change.     

Simultaneous confidence bands for nonlinear regression models

The maximization and minimization procedure for constructing confidence bands about general regression models is explained. Then, using an existing confidence region about the parameters of a nonlinear regression model and the maximization and minimization procedure, a generally conservative simultaneous confidence band is constructed about the model. Two examples are given, and some problems with the procedure are discussed     

American option pricing under double Heston stochastic volatility model: simulation and strong convergence analysis

In this work, we investigate the double Heston model dynamics which is defined by two independent variance processes with non-Lipschitz diffusions. Next, it is analysed the strong convergence of the volatility processes of the double Heston model. Then, we examine the LSM algorithm to determine the American style option price in the double Heston model. Besides, by performing the antithetic simulation in the original LSM algorithm, we aim to accelerate the algorithm. The accuracy and speed of two algorithms are studied and at last, by some numerical results illustrate the accuracy of the proposed algorithms and examine the effect of the different parameters of the model on the value of the option.     

Reliability estimation in a multicomponent stress–strength model under generalized half-normal distribution based on progressive type-II censoring

In this paper, based on progressively Type-II censored samples, the problem of estimation of multicomponent stress–strength reliability under generalized half-normal (GHN) distribution is considered. The reliability of a k-component stress-strength system is estimated when both stress and strength variates are assumed to have a GHN distribution with various cases of same and different shape and scale parameters. Different methods such as the maximum likelihood estimates (MLEs) and Bayes estimation are discussed. The expectation maximization algorithm and approximate maximum likelihood methods are proposed to compute the MLE of reliability. The Lindley's approximation method, as well as Metropolis–Hastings algorithm, are applied to compute Bayes estimates. The performance of the proposed procedures is also demonstrated via a Monte Carlo simulation study and an illustrative example.     

Trace of the Variance-Covariance Matrix in Natural Exponential Families

In this article, we introduce the notion of trace variance function which is the trace of the variance-covariance matrix. Under some conditions, we prove that this trace variance function characterizes the Natural Exponential Family (NEF). We apply this characterization in order to estimate the distribution which belongs to some NEFs. Therefore, we introduce the estimator of this trace variance function. We give the asymptotic properties of this estimator. Finally, we illustrate our results using a simulation study.     

A fractional version of the Cox–Ingersoll–Ross interest rate model and pricing double barrier option with Hurst index H∈(23,1)

In this work, we study the existence and uniqueness of the solution to a fractional version of the Cox–Ingersoll–Ross (fCIR) stochastic differential equation. The strong convergence of this equation is analyzed and according to it’s framework, we obtain the price of the double barrier option under transaction cost. Finally, we verify the effect of the parameters of the model on the value of the option.     

Board of directors and insider trading with share repurchase programs

This study examines the relationship between insider trading and board of directors’ characteristics around stock repurchase announcements. It is hypothesized that information disparity between insiders and shareholders from share repurchases announcements creates opportunities for insiders to time their trading. However, there is little evidence on whether board characteristics reduce the probability of insider trading with repurchase announcements. Using data from firms listed on Forbes 500 between 1998 and 2004, we found that insider trading is related to share repurchase announcements. In order to focus on board of directors’ role, we controlled for board characteristics that may change the repurchase decision. Our results provide corroborative evidence of insider net selling around repurchase announcements and the advantages of board characteristics to offset insider trading. In particular, the results suggest that board tenure, directors’ ownership and board directorship change positively insider trading around repurchase announcements. Hence, Securities and Exchange Commission should consider rigorous restrictions on share repurchase announcements and be aware of potential influence of the corporate governance device.