Journal of Time Series Analysis · 2013 · 62 citations · 16 references
Natural SciencesDemographyEconometricsProbability TheoryStochastic ProcessesDensity EstimationEstimation TheoryInferential StatisticsLaw Of Large NumbersLimit TheoremsStatistical InferenceAsymptotic Distribution TheoryAsymptotic TheoryCentral Limit TheoremsDensityDensity IndependenceHeavy-tailed Limit TheoremsLimit LawsLimit Theorems For Dependent Random Variables
We present an elaboration of the usual binomial AR(1) process on {0,1, … , N }that allows the thinning probabilities to depend on the current state N only through the ‘density’ n ∕ N , a natural assumption in many real contexts. We derive some basic properties of the model and explore approaches to parameter estimation. Some special cases are considered that allow for overdispersion and underdispersion, as well as positive and negative autocorrelations. We derive a law of large numbers and a central limit theorem, which provide useful large‐ N approximations for various quantities of interest.
16
Epidemic Modelling: An Introduction
Derek A. Zelmer, D. J. Daley, J. Gani · Journal of Parasitology · 2000 · 639 citations · Full text
Statistical Inference for Markov Processes
Ronald Pyke, Patrick Billingsley · Technometrics · 1963 · 565 citations
René Ferland, Alain Latour, Driss Oraichi · Journal of Time Series Analysis · 2006 · 500 citations