A rigorous but gentle introduction for economists

This open access textbook is the first to provide Business and Economics Ph.D. students with a precise and intuitive introduction to the formal backgrounds of modern financial theory. It explains Brownian motion, random processes, measures, and Lebesgue integrals intuitively, but without sacrificing the necessary mathematical formalism, making them accessible for readers with little or no previous knowledge of the field. It also includes mathematical definitions and the hidden stories behind the terms discussing why the theories are presented in specific ways.

Characterization of count data distributions involving additivity and binomial subsampling

In this paper we characterize all the $r$-parameter families of count distributions (satisfying mild conditions) that are closed under addition and under binomial subsampling. Surprisingly, few families satisfy both properties and the resulting models consist of the $r$th-order univariate Hermite distributions. Among these, we find the Poisson ($r=1$) and the ordinary Hermite distributions ($r=2$).Comment: Published at http://dx.doi.org/10.3150/07-BEJ6021 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Statistical Methods Applicable to Selected Problems in Fisheries Biology and Economics

The methods by which fishery scientists estimate fish population size are reviewed. These include tagging, cohort analysis, random sampling and catch per unit of effort indexes. Elementary statistical considerations are introduced to discuss some of the properties of the methods. For example, we model the effect of spatial patchiness on random sampling and the effect of sample size on tagging estimates. Next, the Poisson, negative binomial, and gamma distributions and their interrelations are discussed. In particular, these three distributions form a do-it-yourself kit for making models of the fisherman's catch per trip. One policy tool suggested for fishery regulation is the auction of licenses. Smiley has extended the bidding models of Wilson and Rothkopf and applied them to empirical data on offshore oil lease bids. We discuss Smiley's model, which could provide information about fishermen's expectations and learning about abundance, if and when auction schemes are implemented for fisheries. Finally, we review the application of logit estimation as a tool for studying the discrete choice behavior of fishermen.Environmental Economics and Policy, International Development, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, Risk and Uncertainty,

The Brownian Motion

This open access textbook is the first to provide Business and Economics Ph.D. students with a precise and intuitive introduction to the formal backgrounds of modern financial theory. It explains Brownian motion, random processes, measures, and Lebesgue integrals intuitively, but without sacrificing the necessary mathematical formalism, making them accessible for readers with little or no previous knowledge of the field. It also includes mathematical definitions and the hidden stories behind the terms discussing why the theories are presented in specific ways

Beating the random assignment on constraint satisfaction problems of bounded degree

We show that for any odd $k$ and any instance of the Max-kXOR constraint satisfaction problem, there is an efficient algorithm that finds an assignment satisfying at least a $\frac{1}{2} + \Omega(1/\sqrt{D})$ fraction of constraints, where $D$ is a bound on the number of constraints that each variable occurs in. This improves both qualitatively and quantitatively on the recent work of Farhi, Goldstone, and Gutmann (2014), which gave a \emph{quantum} algorithm to find an assignment satisfying a $\frac{1}{2} + \Omega(D^{-3/4})$ fraction of the equations. For arbitrary constraint satisfaction problems, we give a similar result for "triangle-free" instances; i.e., an efficient algorithm that finds an assignment satisfying at least a $\mu + \Omega(1/\sqrt{D})$ fraction of constraints, where $\mu$ is the fraction that would be satisfied by a uniformly random assignment.Comment: 14 pages, 1 figur