CalhounCalhoun 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:802019-07-22T13:59:00Z2019-07-22T13:59:00ZModelling and Residual Analysis of Nonlinear Autoregressive Time Series in Exponential VariablesLawrance, A.J.Lewis, P.A.W.http://hdl.handle.net/10945/625452019-07-19T20:09:38Z1985-01-01T00:00:00ZModelling 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.
1985-01-01T00:00:00ZThe Exponential Autoregressive-Moving Average EARMA (p,q) ProcessLawrance, A.J.Lewis, P.A.W.http://hdl.handle.net/10945/625442019-07-19T19:54:23Z1980-01-01T00:00:00ZThe 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.
1980-01-01T00:00:00ZSecuring Agent 111, and the Job of Software ArchitectArquilla, JohnBugayenko, Yegorhttp://hdl.handle.net/10945/625432019-07-19T18:03:53Z2018-12-01T00:00:00ZSecuring 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
2018-12-01T00:00:00ZNonparametric Estimation of the Probability of a Long Delay in the M/G/1 QueueGaver, D.P.Jacobs, P.A.http://hdl.handle.net/10945/625422019-07-19T17:44:15Z1988-01-01T00:00:00ZNonparametric 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