Selected Works of C.C. Heyde

Author’s Pick.- Chris Heyde’s Contribution to Inference in Stochastic Processes.- Chris Heyde’s Work on Rates of Convergence in the Central Limit Theorem.- Chris Heyde’s Work in Probability Theory, with an Emphasis on the LIL.- Chris Heyde on Branching Processes and Population Genetics.- On a Property of the Lognormal Distribution.- Two Probability Theorems and Their Application to Some First Passage Problems.- Some Renewal Theorems with Application to a First Passage Problem.- Some Results on Small-Deviation Probability Convergence Rates for Sums of Independent Random Variables.- A Contribution to the Theory of Large Deviations for Sums of Independent Random Variables.- On Large Deviation Problems for Sums of Random Variables which are not Attracted to the Normal Law.- On the Influence of Moments on the Rate of Convergence to the Normal Distribution.- On Large Deviation Probabilities in the Case of Attraction to a Non-Normal Stable Law.- On the Converse to the Iterated Logarithm Law.- A Note Concerning Behaviour of Iterated Logarithm Type.- On Extended Rate of Convergence Results for the Invariance Principle.- On the Maximum of Sums of Random Variables and the Supremum Functional for Stable Processes.- Some Properties of Metrics in a Study on Convergence to Normality.- Extension of a Result of Seneta for the Super-Critical Galton–Watson Process.- On the Implication of a Certain Rate of Convergence to Normality.- A Rate of Convergence Result for the Super-Critical Galton-Watson Process.- On the Departure from Normality of a Certain Class of Martingales.- Some Almost Sure Convergence Theorems for Branching Processes.- Some Central Limit Analogues for Supercritical Galton-Watson Processes.- An Invariance Principle and Some Convergence Rate Results for BranchingProcesses.- Improved classical limit analogues for Galton-Watson processes with or without immigration.- Analogues of Classical Limit Theorems for the Supercritical Galton-Watson Process with Immigration.- On Limit Theorems for Quadratic Functions of Discrete Time Series.- Martingales: A Case for a Place in the Statistician’s Repertoire.- On the Influence of Moments on Approximations by Portion of a Chebyshev Series in Central Limit Convergence.- Estimation Theory for Growth and Immigration Rates in a Multiplicative Process.- An Iterated Logarithm Result for Martingales and its Application in Estimation Theory for Autoregressive Processes.- On the Uniform Metric in the Context of Convergence to Normality.- Invariance Principles for the Law of the Iterated Logarithm for Martingales and Processes with Stationary Increments.- An Iterated Logarithm Result for Autocorrelations of a Stationary Linear Process.- On Estimating the Variance of the Offspring Distribution in a Simple Branching Process.- A Nonuniform Bound on Convergence to Normality.- Remarks on efficiency in estimation for branching processes.- The Genetic Balance between Random Sampling and Random Population Size.- On a unified approach to the law of the iterated logarithm for martingales.- The Effect of Selection on Genetic Balance when the Population Size is Varying.- On Central Limit and Iterated Logarithm Supplements to the Martingale Convergence Theorem.- A Log Log Improvement to the Riemann Hypothesis for the Hawkins Random Sieve.- On an Optimal Asymptotic Property of the Maximum Likelihood Estimator of a Parameter from a Stochastic Process.- On Asymptotic Posterior Normality for Stochastic Processes.- On the Survival of a Gene Represented in a Founder Population.- An alternative approach to asymptoticresults on genetic composition when the population size is varying.- On the Asymptotic Equivalence of Lp Metrics for Convergence to Normality.- Quasi-likelihood and Optimal Estimation.- Fisher Lecture.- On Best Asymptotic Confidence Intervals for Parameters of Stochastic Processes.- A quasi-likelihood approach to estimating parameters in diffusion-type processes.- Asymptotic Optimality.- On Defining Long-Range Dependence.- A Risky Asset Model with Strong Dependence through Fractal Activity Time.- Statistical estimation of nonstationary Gaussian processes with long-range dependence and intermittency.