Calhoun Calhoun is the Naval Postgraduate School's digital repository for research materials and institutional publications created by the NPS community. Materials in Calhoun are openly accessible to anyone on the web, and will be preserved for future generations. http://calhoun.nps.edu:80 2019-07-22T13:59:00Z 2019-07-22T13:59:00Z Modelling and Residual Analysis of Nonlinear Autoregressive Time Series in Exponential Variables Lawrance, A.J. Lewis, P.A.W. http://hdl.handle.net/10945/62545 2019-07-19T20:09:38Z 1985-01-01T00:00:00Z Modelling and Residual Analysis of Nonlinear Autoregressive Time Series in Exponential Variables Lawrance, A.J.; Lewis, P.A.W. An approach to modelling and residual analysis of nonlinear autoregressive time series in exponential variables is presented; the approach is illustrated by an analysis of a long series of wind velocity data which has first been detrended and then transformed into a stationary series with an exponential marginal distribution. The stationary series is modelled with a newly developed type of second order autoregressive process with random coefficients, called the NEAR(2) model; it has a second order autoregressive correlation structure but is nonlinear because its coefficients are random. The exponential distributional assumptions involved in this model highlight a very broad four parameter structure which combines five exponential random variables into a sixth exponential random variable; other applications of this structure are briefly considered. Dependency in the NEAR(2) process not accounted for by standard autocorrelations is explored by developing a residual analysis for time series having autoregressive correlation structure; this involves defining linear uncorrelated residuals which are dependent, and then assessing this higher order dependence by standard time series computations. The application of this residual analysis to the wind velocity data illustrates both the utility and difficulty of nonlinear time series modelling. 1985-01-01T00:00:00Z The Exponential Autoregressive-Moving Average EARMA (p,q) Process Lawrance, A.J. Lewis, P.A.W. http://hdl.handle.net/10945/62544 2019-07-19T19:54:23Z 1980-01-01T00:00:00Z The Exponential Autoregressive-Moving Average EARMA (p,q) Process Lawrance, A.J.; Lewis, P.A.W. A new model for pth‐order autoregressive processes with exponential marginal distributions, ear(p), is developed and an earlier model for first‐order moving average exponential processes is extended to qth‐order, giving an ema(q) process. The correlation structures of both processes are obtained separately. A mixed process, earma(p,q), incorporating aspects of both ear(p) and ema(q) correlation structures is then developed. The earma(p, q) process is an analog of the standard arma(p, q) time series models for Gaussian processes and is generated from a single sequence of independent and identically distributed exponential varables. 1980-01-01T00:00:00Z Securing Agent 111, and the Job of Software Architect Arquilla, John Bugayenko, Yegor http://hdl.handle.net/10945/62543 2019-07-19T18:03:53Z 2018-12-01T00:00:00Z Securing Agent 111, and the Job of Software Architect Arquilla, John; Bugayenko, Yegor John Arquilla describes the new state of cyberspying, while Yegor Bugayenko considers the importance of a software architect to development projects. The article of record as published may be found at http://dx.doi.org/10.1145/3282874 2018-12-01T00:00:00Z Nonparametric Estimation of the Probability of a Long Delay in the M/G/1 Queue Gaver, D.P. Jacobs, P.A. http://hdl.handle.net/10945/62542 2019-07-19T17:44:15Z 1988-01-01T00:00:00Z Nonparametric Estimation of the Probability of a Long Delay in the M/G/1 Queue Gaver, D.P.; Jacobs, P.A. A Poisson stream of customers with known arrival rate ʎ approaches a single server having independent identically distributed service times with unknown distribution. A nonpara- metric estimator of the probability of a long customer delay is obtained from an asymptotic renewal theoretic result giving an exponential approximation to the tail of the virtual waiting time distribution for a stable M/G/1 queue. Asymptotic properties of the estimator are obtained. Results of a simulation study of the small sample size behaviour are given. 1988-01-01T00:00:00Z