Kasta gris är ett spel där spelarna tävlar om att komma först till 100 poäng. Två grisformade tärningar kastas och beroende på hur de landar ger de olika poäng, alternativt förlust av poäng. För en spelare som har samlade poäng i en kastomgång innebär ytterligare ett kast en chans att erhålla en högre poängsumma men också en risk att förlora den redan samlade. I denna uppsats vill vi ta reda på vid vilken högsta poängsumma i en kastomgång som spelaren bör välja att fortsätta kasta. Eftersom tärningarna är grisformade och alltså inte symmetriska är sannolikheterna olika för de möjliga utfallen. Att sannolikheterna därtill är okända omöjliggör att beräkna den sökta poängsumman exakt. Vi har genomfört ett eget försök med 10 517 kast uppdelade på tre gristärningspar. Med hjälp av insamlad data och metoder inom sannolikhetslära har vi kunnat skatta de okända sannolikheterna och därmed den sökta poängsumman. För att få ett mått på osäkerheten i vår skattning av den senare har vi använt två metoder inom inferensteorin, Deltametoden och bootstrap. I vårt resultat fann vi att 21, med åtminstone 75 procents säkerhet, är den högsta poängsumma för vilken en spelare bör fortsätta sin kastomgång. Resultatet ger en spelare möjlighet att maximera sin förväntade poäng i en kastomgång men att använda detta som en spelstrategi genom hela spelet är dock ingen garanti för vinst.

This thesis is concerned with hypothesis testing in models where data exhibits time dependence. The focus is on two cases where the dependence of observations across time leads to non-standard hypothesis testing techniques. This thesis first considers models estimated by Generalized Method of Moments (GMM, Hansen (1982)) and the approach to inference. The main problem with standard tests are size distortions in the test statistics. An innovative resampling method, which we label Empirical Likelihood Block Bootstrapping, is proposed. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping. Also staying in the context of GMM this thesis shows that the testcorrection given in Hall (2000) which improves power, can distort size with time dependent data. In this case it is of even greater importance to use a bootstrap that can have good size in finite samples. The empirical likelihood is applied to a multifactor model of U.S. bank risk estimated by GMM. The approach to inference is found to be important to the overall conclusion about bank risk. The results suggest U.S. bank stock returns are sensitive to movements in market and liquidity risk. In the context of panel data, this thesis is the first to my knowledge to consider the estimation of cost-functions as well as conduct inference taking into account the strong dependence of data across time. This thesis shows that standard approaches to estimating cost-functions for a set of Canadian banks lead to a downward bias in the estimated coefficients and therefore an upward bias in the measure of economies of scale. When non-stationary panel techniques are applied results suggest economies of scale of around 6 per cent in Canadian banking as well as cost-efficiency differences across banks that are correlated with size. / Thesis (Ph.D, Economics) -- Queen's University, 2007-09-24 17:25:22.212

We consider the use of a sieve bootstrap based on moving average (MA) and autoregressive moving average (ARMA) approximations to test the unit root hypothesis when the true Data Generating Process (DGP) is a general linear process. We provide invariance principles for these bootstrap DGPs and we prove that the resulting ADF tests are asymptotically valid. Our simulations indicate that these tests sometimes outperform those based on the usual autoregressive (AR) sieve bootstrap. We study the reasons for the failure of the AR sieve bootstrap tests and propose some solutions, including a modified version of the fast double bootstrap. / We also argue that using biased estimators to build bootstrap DGPs may result in less accurate inference. Some simulations confirm this in the case of ADF tests. We show that one can use the GLS transformation matrix to obtain equations that can be used to estimate bias in general ARMA(p,q) models. We compare the resulting bias reduced estimator to a widely used bootstrap based bias corrected estimator. Our simulations indicate that the former has better finite sample properties then the latter in the case of MA models. Finally, our simulations show that using bias corrected or bias reduced estimators to build bootstrap DGP sometimes provides accuracy gains.

Thesis (M.Eng.) - Carleton University, 2002. / Includes bibliographical references (p. 130-135). Also available in electronic format on the Internet.

Thesis (Ph. D.)--Physics, Georgia Institute of Technology, 2005. / You, Li, Committee Member ; Buck, John A., Committee Member ; Kvam, Paul, Committee Member ; Kennedy, Brian, Committee Member ; Trebino, Rick, Committee Chair. Vita. Includses bibliographical references.

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. James J. O'Brien, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Jan. 12, 2005). Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 2000. / Vita. Includes bibliographical references (leaves 206-209).

Zugl.: Braunschweig, Techn. Univ., Diss., 2009

Thesis (M.S. in Operations Research) Naval Postgraduate School, March 1997. / Thesis advisor, Donald P. Gaver. Includes bibliographical references (p. 35). Also available online.

University, Diss., 2002--Dortmund.