## Elements of Modern Asymptotic Theory with Statistical Applications |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Common terms and phrases

a-field almost-sure convergence applies argument assumed assumption asymptotic distribution asymptotic normality Brownian bridge Brownian motion Chapter consider constructed context continuous convergence in distribution convergence in probability converges to zero countably infinite covariance defined definition density function dependent discussion distribution function Donsker's Theorem evaluated Example exist expectation finite follows given Hence identically distributed implies independent inequality infinite number interval Lebesgue measure lim sup limit random variable limiting distribution Lindeberg condition mapping Markov's inequality martingale matrix mean and variance mixing moments multivariate normal distribution normal random variable notation null hypothesis outcomes parameter partial-sum process possible probability measure probability space properties random function random sample real line result sample space score equation Section sequence of random sigma-field squares standardised stationary statistic Stieltjes integral stochastic process subsets tion variance a2 vector random variable weak convergence WLLN zero mean