Selected Works of C.C. Heyde

Table of Contents

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   Book Overview
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   Chapter 1 Author’s Pick
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   Chapter 2 Chris Heyde’s Contribution to Inference in Stochastic Processes
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   Chapter 3 Chris Heyde’s Work on Rates of Convergence in the Central Limit Theorem
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   Chapter 4 Chris Heyde’s Work in Probability Theory, with an Emphasis on the LIL
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   Chapter 5 Chris Heyde on Branching Processes and Population Genetics
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   Chapter 6 On a Property of the Lognormal Distribution
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   Chapter 7 Two Probability Theorems and Their Application to Some First Passage Problems
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   Chapter 8 Some Renewal Theorems with Application to a First Passage Problem
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    Chapter 9 Some Results on Small-Deviation Probability Convergence Rates for Sums of Independent Random Variables
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    Chapter 10 A Contribution to the Theory of Large Deviations for Sums of Independent Random Variables
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    Chapter 11 On Large Deviation Problems for Sums of Random Variables which are not Attracted to the Normal Law
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    Chapter 12 On the Influence of Moments on the Rate of Convergence to the Normal Distribution
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    Chapter 13 On Large Deviation Probabilities in the Case of Attraction to a Non-Normal Stable Law
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    Chapter 14 On the Converse to the Iterated Logarithm Law
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    Chapter 15 A Note Concerning Behaviour of Iterated Logarithm Type
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    Chapter 16 On Extended Rate of Convergence Results for the Invariance Principle
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    Chapter 17 On the Maximum of Sums of Random Variables and the Supremum Functional for Stable Processes
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    Chapter 18 Some Properties of Metrics in a Study on Convergence to Normality
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    Chapter 19 Extension of a Result of Seneta for the Super-Critical Galton–Watson Process
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    Chapter 20 On the Implication of a Certain Rate of Convergence to Normality
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    Chapter 21 A Rate of Convergence Result for the Super-Critical Galton-Watson Process
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    Chapter 22 On the Departure from Normality of a Certain Class of Martingales
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    Chapter 23 Some Almost Sure Convergence Theorems for Branching Processes
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    Chapter 24 Some Central Limit Analogues for Supercritical Galton-Watson Processes
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    Chapter 25 An Invariance Principle and Some Convergence Rate Results for Branching Processes
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    Chapter 26 Improved classical limit analogues for Galton-Watson processes with or without immigration
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    Chapter 27 Analogues of Classical Limit Theorems for the Supercritical Galton-Watson Process with Immigration
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    Chapter 28 On Limit Theorems for Quadratic Functions of Discrete Time Series
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    Chapter 29 Martingales: A Case for a Place in the Statistician’s Repertoire
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    Chapter 30 On the Influence of Moments on Approximations by Portion of a Chebyshev Series in Central Limit Convergence
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    Chapter 31 Estimation Theory for Growth and Immigration Rates in a Multiplicative Process
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    Chapter 32 An Iterated Logarithm Result for Martingales and its Application in Estimation Theory for Autoregressive Processes
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    Chapter 33 On the Uniform Metric in the Context of Convergence to Normality
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    Chapter 34 Invariance Principles for the Law of the Iterated Logarithm for Martingales and Processes with Stationary Increments
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    Chapter 35 An Iterated Logarithm Result for Autocorrelations of a Stationary Linear Process
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    Chapter 36 On Estimating the Variance of the Offspring Distribution in a Simple Branching Process
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    Chapter 37 A Nonuniform Bound on Convergence to Normality
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    Chapter 38 Remarks on efficiency in estimation for branching processes
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    Chapter 39 The Genetic Balance between Random Sampling and Random Population Size
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    Chapter 40 On a unified approach to the law of the iterated logarithm for martingales
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    Chapter 41 The Effect of Selection on Genetic Balance when the Population Size is Varying
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    Chapter 42 On Central Limit and Iterated Logarithm Supplements to the Martingale Convergence Theorem
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    Chapter 43 A Log Log Improvement to the Riemann Hypothesis for the Hawkins Random Sieve
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    Chapter 44 On an Optimal Asymptotic Property of the Maximum Likelihood Estimator of a Parameter from a Stochastic Process
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    Chapter 45 On Asymptotic Posterior Normality for Stochastic Processes
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    Chapter 46 On the Survival of a Gene Represented in a Founder Population
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    Chapter 47 An alternative approach to asymptotic results on genetic composition when the population size is varying
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    Chapter 48 On the Asymptotic Equivalence of L p Metrics for Convergence to Normality
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    Chapter 49 Quasi-likelihood and Optimal Estimation
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    Chapter 50 Fisher Lecture
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    Chapter 51 On Best Asymptotic Confidence Intervals for Parameters of Stochastic Processes
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    Chapter 52 A quasi-likelihood approach to estimating parameters in diffusion-type processes
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    Chapter 53 Asymptotic Optimality
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    Chapter 54 On Defining Long-Range Dependence
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    Chapter 55 A Risky Asset Model with Strong Dependence through Fractal Activity Time
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    Chapter 56 Statistical estimation of nonstationary Gaussian processes with long-range dependence and intermittency