arXiv subjects

Glenn Shafer

Publications and source records attributed to Glenn Shafer.

At least 19 recordsLinked to original sources

arXiv2023

A Conversation with A. Philip Dawid

Beginning in the 1970s, Alexander Philip Dawid has been a leading contributor to the foundations of statistics and especially to the development and application of Bayesian statistics. He is also known for his work on causality, especially his notation for conditional independence and his critique of the overuse of counterfactuals, and for his contributions to forensic statistics. Dawid was born in Lancashire, England, on February 1, 1946. His family moved to London soon afterwards, and he attended the City of London School from 1956 to 1963. He studied mathematics at Cambridge, earning a BA (Bachelor of Arts) degree in 1966. After earning a Diploma in Mathematical Statistics in the academic year 1966-1967, he studied for a PhD at Imperial, then at UCL, where he became a Lecturer in Statistics in 1969. In 1978, he left UCL for a position as Professor of Statistics in the Department of Mathematics, The City University, London, where he served as Head of Statistics Section and Director of the Statistical Laboratory. He returned to the Department of Statistics at UCL in 1981, serving as Head of Department from 1983 to 1993. He moved to the University of Cambridge in 2007, becoming Professor of Statistics and Fellow of Darwin College. He has continued his work in mathematical statistics after retiring from Cambridge in 2013 and was elected Fellow of the Royal Society in 2018.

stat.OT↗

arXiv2023

Two ways game-theoretic probability can improve data analysis

When testing a statistical hypothesis, is it legitimate to deliberate on the basis of initial data about whether and how to collect further data? Game-theoretic probability's fundamental principle for testing by betting says yes, provided that you are testing by betting and do not risk more capital than initially committed. Standard statistical theory uses Cournot's principle, which does not allow such optional continuation. Cournot's principle can be extended to allow optional continuation when testing is carried out by multiplying likelihood ratios, but the extension lacks the simplicity and generality of testing by betting. Game-theoretic probability can also help us with descriptive data analysis. To obtain a purely and honestly descriptive analysis using competing probability distributions, we have them bet against each other using the Kelly principle. The place of confidence intervals is then taken by a sets of distributions that do relatively well in the competition. In the simplest implementation, these sets coincide with R. A. Fisher's likelihood intervals.

stat.ME↗

arXiv2023

Game-theoretic statistics and safe anytime-valid inference

Safe anytime-valid inference (SAVI) provides measures of statistical evidence and certainty -- e-processes for testing and confidence sequences for estimation -- that remain valid at all stopping times, accommodating continuous monitoring and analysis of accumulating data and optional stopping or continuation for any reason. These measures crucially rely on test martingales, which are nonnegative martingales starting at one. Since a test martingale is the wealth process of a player in a betting game, SAVI centrally employs game-theoretic intuition, language and mathematics. We summarize the SAVI goals and philosophy, and report recent advances in testing composite hypotheses and estimating functionals in nonparametric settings.

math.ST↗

arXiv2019

The Language of Betting as a Strategy for Statistical and Scientific Communication

The established language for statistical testing --- significance levels, power, and p-values --- is overly complicated and deceptively conclusive. Even teachers of statistics and scientists who use statistics misinterpret the results of statistical tests, tending to misstate their meaning and exaggerate their certainty. We can communicate the meaning and limitations of statistical evidence more clearly using the language of betting. This paper calls attention to a simple betting interpretation of likelihood ratios. This interpretation leads to methods that lend themselves to meta-analysis and accounting for multiple testing. It is closely related to the interpretation of probability as frequency, but it does not encourage the fallacy that probabilistic models imply the existence of unseen alternative worlds. For more on the betting interpretation of probability, see \cite{Shafer/Vovk:2019} and the other working papers at www.probabilityandfinance.com.

math.ST↗

arXiv2018

Game-Theoretic Capital Asset Pricing in Continuous Time

We derive formulas for the performance of capital assets in continuous time from an efficient market hypothesis, with no stochastic assumptions and no assumptions about the beliefs or preferences of investors. Our efficient market hypothesis says that a speculator with limited means cannot beat a particular index by a substantial factor. Our results include a formula that resembles the classical CAPM formula for the expected simple return of a security or portfolio. This version of the article was essentially written in December 2001 but remains a working paper.

q-fin.PR↗

arXiv2018

The origins and legacy of Kolmogorov's Grundbegriffe

April 25, 2003, marked the 100th anniversary of the birth of Andrei Nikolaevich Kolmogorov, the twentieth century's foremost contributor to the mathematical and philosophical foundations of probability. The year 2003 was also the 70th anniversary of the publication of Kolmogorov's Grundbegriffe der Wahrscheinlichkeitsrechnung. Kolmogorov's Grundbegriffe put probability's modern mathematical formalism in place. It also provided a philosophy of probability - an explanation of how the formalism can be connected to the world of experience. In this article, we examine the sources of these two aspects of the Grundbegriffe - the work of the earlier scholars whose ideas Kolmogorov synthesized.

math.HO↗

arXiv2018

A game-theoretic derivation of the $\sqrt{dt}$ effect

We study the origins of the $\sqrt{dt}$ effect in finance and SDE. In particular, we show, in the game-theoretic framework, that market volatility is a consequence of the absence of riskless opportunities for making money and that too high volatility is also incompatible with such opportunities. More precisely, riskless opportunities for making money arise whenever a traded security has fractal dimension below or above that of the Brownian motion and its price is not almost constant and does not become extremely large. This is a simple observation known in the measure-theoretic mathematical finance. At the end of the article we also consider the case of non-zero interest rate. This version of the article was essentially written in March 2005 but remains a working paper.

q-fin.MF↗

arXiv2017

Towards a probability-free theory of continuous martingales

Without probability theory, we define classes of supermartingales, martingales, and semimartingales in idealized financial markets with continuous price paths. This allows us to establish probability-free versions of a number of standard results in martingale theory, including the Dubins-Schwarz theorem, the Girsanov theorem, and results concerning the Itô integral. We also establish the existence of an equity premium and a CAPM relationship in this probability-free setting.

q-fin.MF↗

arXiv2016

A probability-free and continuous-time explanation of the equity premium and CAPM

This paper gives yet another definition of game-theoretic probability in the context of continuous-time idealized financial markets. Without making any probabilistic assumptions (but assuming positive and continuous price paths), we obtain a simple expression for the equity premium and derive a version of the capital asset pricing model.

q-fin.MF↗

arXiv2013

Valuation-Based Systems for Discrete Optimization

This paper describes valuation-based systems for representing and solving discrete optimization problems. In valuation-based systems, we represent information in an optimization problem using variables, sample spaces of variables, a set of values, and functions that map sample spaces of sets of variables to the set of values. The functions, called valuations, represent the factors of an objective function. Solving the optimization problem involves using two operations called combination and marginalization. Combination tells us how to combine the factors of the joint objective function. Marginalization is either maximization or minimization. Solving an optimization problem can be simply described as finding the marginal of the joint objective function for the empty set. We state some simple axioms that combination and marginalization need to satisfy to enable us to solve an optimization problem using local computation. For optimization problems, the solution method of valuation-based systems reduces to non-serial dynamic programming. Thus our solution method for VBS can be regarded as an abstract description of dynamic programming. And our axioms can be viewed as conditions that permit the use of dynamic programming.

cs.AI↗

arXiv2013

An Axiomatic Framework for Bayesian and Belief-function Propagation

In this paper, we describe an abstract framework and axioms under which exact local computation of marginals is possible. The primitive objects of the framework are variables and valuations. The primitive operators of the framework are combination and marginalization. These operate on valuations. We state three axioms for these operators and we derive the possibility of local computation from the axioms. Next, we describe a propagation scheme for computing marginals of a valuation when we have a factorization of the valuation on a hypertree. Finally we show how the problem of computing marginals of joint probability distributions and joint belief functions fits the general framework.

cs.AI↗

arXiv2013

Modifiable Combining Functions

Modifiable combining functions are a synthesis of two common approaches to combining evidence. They offer many of the advantages of these approaches and avoid some disadvantages. Because they facilitate the acquisition, representation, explanation, and modification of knowledge about combinations of evidence, they are proposed as a tool for knowledge engineers who build systems that reason under uncertainty, not as a normative theory of evidence.

cs.AI↗

arXiv2013

Propagation of Belief Functions: A Distributed Approach

In this paper, we describe a scheme for propagating belief functions in certain kinds of trees using only local computations. This scheme generalizes the computational scheme proposed by Shafer and Logan1 for diagnostic trees of the type studied by Gordon and Shortliffe, and the slightly more general scheme given by Shafer for hierarchical evidence. It also generalizes the scheme proposed by Pearl for Bayesian causal trees (see Shenoy and Shafer). Pearl's causal trees and Gordon and Shortliffe's diagnostic trees are both ways of breaking the evidence that bears on a large problem down into smaller items of evidence that bear on smaller parts of the problem so that these smaller problems can be dealt with one at a time. This localization of effort is often essential in order to make the process of probability judgment feasible, both for the person who is making probability judgments and for the machine that is combining them. The basic structure for our scheme is a type of tree that generalizes both Pearl's and Gordon and Shortliffe's trees. Trees of this general type permit localized computation in Pearl's sense. They are based on qualitative judgments of conditional independence. We believe that the scheme we describe here will prove useful in expert systems. It is now clear that the successful propagation of probabilities or certainty factors in expert systems requires much more structure than can be provided in a pure production-system framework. Bayesian schemes, on the other hand, often make unrealistic demands for structure. The propagation of belief functions in trees and more general networks stands on a middle ground where some sensible and useful things can be done. We would like to emphasize that the basic idea of local computation for propagating probabilities is due to Judea Pearl. It is a very innovative idea; we do not believe that it can be found in the Bayesian literature prior to Pearl's work. We see our contribution as extending the usefulness of Pearl's idea by generalizing it from Bayesian probabilities to belief functions. In the next section, we give a brief introduction to belief functions. The notions of qualitative independence for partitions and a qualitative Markov tree are introduced in Section III. Finally, in Section IV, we describe a scheme for propagating belief functions in qualitative Markov trees.

cs.AI↗

arXiv2013

Probability Judgement in Artificial Intelligence

This paper is concerned with two theories of probability judgment: the Bayesian theory and the theory of belief functions. It illustrates these theories with some simple examples and discusses some of the issues that arise when we try to implement them in expert systems. The Bayesian theory is well known; its main ideas go back to the work of Thomas Bayes (1702-1761). The theory of belief functions, often called the Dempster-Shafer theory in the artificial intelligence community, is less well known, but it has even older antecedents; belief-function arguments appear in the work of George Hooper (16401723) and James Bernoulli (1654-1705). For elementary expositions of the theory of belief functions, see Shafer (1976, 1985).

cs.AI↗

arXiv2011

Probability-free pricing of adjusted American lookbacks

Consider an American option that pays G(X^*_t) when exercised at time t, where G is a positive increasing function, X^*_t := \sup_{s\le t}X_s, and X_s is the price of the underlying security at time s. Assuming zero interest rates, we show that the seller of this option can hedge his position by trading in the underlying security if he begins with initial capital X_0\int_{X_0}^{\infty}G(x)x^{-2}dx (and this is the smallest initial capital that allows him to hedge his position). This leads to strategies for trading that are always competitive both with a given strategy's current performance and, to a somewhat lesser degree, with its best performance so far. It also leads to methods of statistical testing that avoid sacrificing too much of the maximum statistical significance that they achieve in the course of accumulating data.

q-fin.PR↗

arXiv2011

Test Martingales, Bayes Factors and $p$-Values

A nonnegative martingale with initial value equal to one measures evidence against a probabilistic hypothesis. The inverse of its value at some stopping time can be interpreted as a Bayes factor. If we exaggerate the evidence by considering the largest value attained so far by such a martingale, the exaggeration will be limited, and there are systematic ways to eliminate it. The inverse of the exaggerated value at some stopping time can be interpreted as a $p$-value. We give a simple characterization of all increasing functions that eliminate the exaggeration.

math.ST↗

arXiv2011

Levy's zero-one law in game-theoretic probability

We prove a game-theoretic version of Levy's zero-one law, and deduce several corollaries from it, including non-stochastic versions of Kolmogorov's zero-one law, the ergodicity of Bernoulli shifts, and a zero-one law for dependent trials. Our secondary goal is to explore the basic definitions of game-theoretic probability theory, with Levy's zero-one law serving a useful role.

math.PR↗

arXiv2010

Insuring against loss of evidence in game-theoretic probability

We consider the game-theoretic scenario of testing the performance of Forecaster by Sceptic who gambles against the forecasts. Sceptic's current capital is interpreted as the amount of evidence he has found against Forecaster. Reporting the maximum of Sceptic's capital so far exaggerates the evidence. We characterize the set of all increasing functions that remove the exaggeration. This result can be used for insuring against loss of evidence.

math.ST↗