Thesis (M.A.) in Mathematics--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaf 34).

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Η παρούσα μεταπτυχιακή διατριβή εντάσσεται ερευνητικά στην περιοχή της Στατιστικής Θεωρίας Αποφάσεων και ειδικότερα στη βελτίωση των εκτιμητών του μέτρου της διασποράς για την αντίστροφη κανονική κατανομή IG(μ,λ), όπου μ είναι η παράμετρος θέσης, ενώ το λ είναι η παράμετρος κλίμακος και εκφράζει το αντίστροφο μέτρο της διασποράς. Στο Κεφάλαιο 1 παρουσιάζονται κάποιοι χρήσιμοι, για την πορεία της μελέτης μας, ορισμοί και θεωρήματα, στο Κεφάλαιο 2 μελετάται το μοντέλο της αντίστροφης κανονικής κατανομής, δίνονται τα χαρακτηριστικά μεγέθη αυτής και υπολογίζονται γνωστοί εκτιμητές για το μέτρο της διασποράς. Στο Κεφάλαιο 3, εξετάζεται το πρόβλημα εκτίμησης του μέτρου της διασποράς , τόσο ως προς την τετραγωνική συνάρτηση ζημίας , όσο και προς την συνάρτηση ζημίας εντροπίας . Υπό ορισμένες συνθήκες, αποδεικνύεται η μη αποδεκτικότητα, του βέλτιστου αναλλοίωτου εκτιμητή, κατασκευάζοντας καλύτερους εκτιμητές τύπου Stein [1964, Ann. Inst. Statist. Math.]. Η μέθοδος κατασκευής αυτών των εκτιμητών παρουσιάστηκε στην εργασία των N. Pal and B. Sinha [1989, Statistical data analysis and inference]. Στο Κεφάλαιο 4, και για το ίδιο πρόβλημα εκτίμησης που πραγματεύεται το προηγούμενο κεφάλαιο,κατασκευάζονται καλύτεροι εκτιμητές από το βέλτιστο αναλλοίωτο εκτιμητή χρησιμοποιώντας την μεθοδολογία των Brewster and Zidek [1974, Ann. Statist.]. Η μέθοδος κατασκευής αυτών των εκτιμητών παρουσιάζεται στην εργασία των B.MacGibbon and G.Shorrock [1997, Statist. Probab. Lett.]. / The present postgraduate thesis is placed among the area of Statistical Decision Theory and especially we give improved estimators of dispersion of an inverse Gaussian distribution IG(μ,λ), where μ is the mean and λ is a parameter, known as inverse measure of dispersion. In Chapter 1 are some useful definitions and theorems are presented, in Chapter 2 the model of inverse Gaussian distribution is studied, we give some properties of the model and known estimators for the measure of dispersion , are presented. In Chapter 3, we examine the problem of estimating the measure of dispersion under quadratic and entropy losses. Under certain conditions, we derive improved estimators (Stein [1964, Ann. Inst. Statist. Math.]) of the best affine equivariant estimator for . The method of construction of these estimators is presented in the paper of N. Pal and B. Sinha [ 1989, Statistical data analysis and inference ]. In Capital 4, and for the same problem of estimating , improved estimators of the best affine equivariant estimator are derived, using the methodology of Brewster and Zidek [1974, Ann. Statist. ]. This method of construction of these estimators is presented in the paper of B. MacGibbon and G. Shorrock [1997, Statist. Probab. Lett. ].

Statistical quality control (SQC) is one technique companies are using in the development of a Total Quality Management (TQM) culture. Shewhart control charts, a widely used SQC tool, rely on an underlying normal distribution of the data. Often data are skewed. The inverse Gaussian distribution is a probability distribution that is wellsuited to handling skewed data. This analysis develops models and a set of tools usable by practitioners for the constrained economic statistical design of control charts for inverse Gaussian distribution process centrality and process dispersion. The use of this methodology is illustrated by the design of an x-bar chart and a V chart for an inverse Gaussian distributed process.

by Wong Kwok Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 50-52). / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Taguchi's methodology in design of experiments --- p.3 / Chapter 2.1 --- System design / Chapter 2.2 --- Parameter design / Chapter 2.3 --- Tolerance design / Chapter 3. --- Inverse Gaussian distribution --- p.8 / Chapter 3.1 --- Genesis / Chapter 3.2 --- Probability density function / Chapter 3.3 --- Estimation of parameters / Chapter 3.4 --- Applications / Chapter 4. --- Iterative procedures and Derivation of the GLIM 4 macros --- p.21 / Chapter 4.1 --- Generalized linear models with varying dispersion / Chapter 4.2 --- Mean and dispersion models for inverse Gaussian distribution / Chapter 4.3 --- Devising the GLIM 4 macro / Chapter 4.4 --- Model fitting / Chapter 5. --- Simulation Study --- p.34 / Chapter 5.1 --- Generating random variates from the inverse Gaussian distribution / Chapter 5.2 --- Simulation model / Chapter 5.3 --- Results / Chapter 5.4 --- Discussion / Appendix --- p.46 / References --- p.50

Thesis (M. Sc. (Mathematical Statistics)) -- University of the North, 1978 / Refer to the document

Thesis (M.Eng.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 71-74. Also available online.

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We demonstrate that for the fast numerical inversion of the (generalized) inverse Gaussian distribution two algorithms based on polynomial interpolation are well-suited. Their precision is close to machine precision and they are much faster than the bisection method recently proposed by Y. Lai. / Series: Research Report Series / Department of Statistics and Mathematics

In this thesis we consider the calibration of models based on Lévy processes to option prices observed in some market. This means that we choose the parameters of the option pricing models such that the prices calculated using the models correspond as closely as possible to these option prices. We demonstrate the ability of relatively simple Lévy option pricing models to nearly perfectly replicate option prices observed in nancial markets. We speci cally consider calibrating option pricing models to barrier option prices and we demonstrate that the option prices obtained under one model can be very accurately replicated using another. Various types of calibration are considered in the thesis. We calibrate a wide range of Lévy option pricing models to option price data. We con- sider exponential Lévy models under which the log-return process of the stock is assumed to follow a Lévy process. We also consider linear Lévy models; under these models the stock price itself follows a Lévy process. Further, we consider time changed models. Under these models time does not pass at a constant rate, but follows some non-decreasing Lévy process. We model the passage of time using the lognormal, Pareto and gamma processes. In the context of time changed models we consider linear as well as exponential models. The normal inverse Gaussian (N IG) model plays an important role in the thesis. The numerical problems associated with the N IG distribution are explored and we propose ways of circumventing these problems. Parameter estimation for this distribution is discussed in detail. Changes of measure play a central role in option pricing. We discuss two well-known changes of measure; the Esscher transform and the mean correcting martingale measure. We also propose a generalisation of the latter and we consider the use of the resulting measure in the calculation of arbitrage free option prices under exponential Lévy models. / PhD (Risk Analysis), North-West University, Potchefstroom Campus, 2015