There are very many processes in the natural and social sciences which can be represented as a set of flows of objects or people between categories of some kind. The Markov chain model has been used in the study of many of them. The basic form of the Markov chain model is, however, rarely adequate to describe social, occupational and geographical mobility processes. We shall therefore discuss a number of generalizations designed to introduce greater realism. In Chapter I we formulate and investigate a general model which results from relaxing the assumptions of sojourn-time's memorylessness and independence of origin and destination states, and of population homogeneity. The model (a mixture of semi-Markov processes) is then used in two ways. First, it provides a framework in which various special cases (which correspond to models which were used by social scientists) can be analytically compared. We pay particular attention to comparisons of rate of mobility in related versions of various models and to compatability of popular parametric forms with observed mobility patterns. Second, any result obtained for the general model can be specialized for the various cases and subcases. In Chapter II we formulate a system-model allowing interaction among individuals (components), which has been motivated by Conlisk. We define processes on this model and analyze their properties. A major effort is then devoted to establishing that when the population size becomes large, this rather complex stochastic model can be approximated by a single deterministic recursion due to Conlisk (1976). Nevertheless, we draw attention to certain aspects (particularly steady-state behavior) in which the approximation may fail. In Chapter III we address ourselves to the issue of measurement of (what we refer to as) social inheritance in intergenerational mobility processes. We distinguish between various aspects and concepts of social inheritance and outline the implications that certain "social values" may have on constructing a measure (or index). In the mathematical discussion which follows certain mechanisms for generating "families" of measures are indicated, and the properties of some particular combinations are investigated. / Business, Sauder School of / Graduate

Numerical models of the atmosphere discretize space and time, and are unable to resolve processes smaller than model resolution. As such, the aggregate effects of these sub-grid scale processes must be parameterized when their effects are manifest at resolved grid scale. However, it is known that the enhancement of sea-surface fluxes by sub-grid scale wind variations is difficult to appropriately parameterize deterministically. This limitation can be realized in a numerical model by the use of stochasticity, explicitly accounting for the randomness in how sea surface wind variability enhances sea-surface fluxes. The robustness of stochastically parameterizing sea surface flux enhancement due to wind speed variability is investigated by applying an established statistical model to coarse grained global convection permitting numerical model output from six different numerical models and four different geographical regions, to determine if there exists any sensitivities to region, time period, or model type. The sensitivity of the deterministic part of a surface flux parameterization studied is quantified via correlation, where different ten-day periods have the highest correlations and thus the least sensitivity, followed by differences in numerical models and differences in geographical regions. Results suggest that the choice of cumulus parameterization employed by a numerical model may contribute to statistical model sensitivity and consequent regression fit portability. The robustness of a Gaussian process fit applied to the stochastic part of the sea-surface flux enhancement parameterization reveals spatial non-stationarity, which provides insight into the potential for further improvements to the sea-surface flux parameterization studied. Results suggest that the stochastic parameterization studied is broadly robust, supporting implementation of such sea surface flux parameterization in operational weather and climate models. Results are also used to identify specific methods that may be utilized for improvements of the stochastic parameterization. / Graduate / 2023-01-27

No description available.

No description available.

The Ito-Stratonovich theory of stochastic integration and stochastic differential equations has several shortcomings, especially when it comes to existence and consistency with the theory of Lebesque-Stieltjes integration and ordinary differential equations. An attempt is made firstly, to isolate the path property, possessed by almost all Brownian paths, that makes the stochastic theory of integration work. Secondly, to construct a new concept of solutions for differential equations, which would have the required consistency and continuity properties, within a class of deterministic noise functions, large enough to include almost all Brownian paths. The algebraic structure of iterated path integrals for smooth paths leads to a formal definition of a solution for a differential equation in terms of generalized path integrals for more general noises. This suggests a way of constructing solutions to differential equations in a large class of paths as limits of operators. The concept of the driving noise is extended to include the generalized path integrals of the noise. Less stringent conditions on the Holder continuity of the path can be compensated by giving more of its iterated integrals. Sufficient conditions for the solution to exist are proved in some special cases, and it is proved that almost all paths of Brownian motion as well as some other stochastic processes can be included in the theory.

Master of Science / Department of Mathematics / Nathan Albin / The term stochastic is defined as having a random probability distribution or pattern that may be analyzed statistically but may not be predicted precisely. A stochastic model attempts to estimate outcomes while allowing a random variation in one or more inputs over time. These models are used across a number of fields from gene expression in biology, to stock, asset, and insurance analysis in finance. In this thesis, we will build up the basic probability theory required to make an ``optimal estimate", as well as construct the stochastic integral. This information will then allow us to introduce stochastic differential equations, along with our overall model. We will conclude with the "optimal estimator", the Kalman Filter, along with an example of its application.

The goal of this paper is to extend the results of Bayraktar and Young (2006) on minimizing an individual's probability of lifetime ruin; i.e. the probability that the individual goes bankrupt before dying. We consider a scenario in which the individual is allowed to invest in both a domestic bank account and a foreign bank account, with the exchange rate between the two currencies being modeled by geometric Brownian motion. Additionally, we impose the restriction that the individual is not allowed to borrow money, and assume that the individual's wealth is consumed at a constant rate. We derive formulas for the minimum probability of ruin as well as the individual's optimal investment strategy. We also give a few numerical examples to illustrate these results.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (Ph. D.)--Kent State University, 2008. / Title from PDF t.p. (viewed Nov. 10, 2009). Advisor: Hassan Allouba. Keywords: stochastic partial differential equations; compact support property. Includes bibliographical references (p. 30).

Not available / text