Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, March 2003. / Thesis advisor(s): Charles W. Therrien, Charles W. Granderson. Includes bibliographical references (p. 123). Also available online.

Thesis (M.A.) in Mathematics--University of Maine, 2001. / Includes vita. Includes bibliographical references (leaves 42-43).

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.

The standard deviation of surface EMG (EMGσ) is often related to muscle force; the accuracy of EMGσ estimation is valuable for many application areas such as clinical biomechanics, prostheses control and sports medicine. Numerous researchers have developed methods to optimize EMGσ estimation. Whitening the EMG signal has been proved to improve the statistical efficiency of EMGσ estimation, but conventional linear whitening filters fail at low contraction level. An adaptive whitening filter was proposed by Clancy and Farry[14]. This technique has a better performance than prior whitening methods, however, the adaptive whitening filter needs to be calibrated each time electrodes are applied, which increase the complexity of the implementation. We designed a universal whitening filter which can omit most calibration steps for the adaptive whitening filter in future use. We used the ensemble mean of the power spectrum of 512 EMG recordings to form a general shape of a fixed whitening filter that can applied on any EMG signal. The test error on an EMG-torque task based on universal whitening over 512 subjects had a mean error of 4.80% maximum voluntary contraction (MVC) and standard deviation (std) of 2.03% MVC, compared with an original adaptive whitening filter error of 4.84±1.98% MVC. Additionally, the rest contraction modeling hasn’t received enough attention. Existing RMS estimates of EMGσ subtract noise in either the amplitude or power domain. These procedures have never been modeled analytically. We show that power domain noise subtraction is optimal. But rest contractions which are processed using power domain noise subtraction only estimate a zero-valued EMGσ approximately 50% of the time, which is undesirable in prosthesis-control. The prosthesis has a 50% possibility to slowly drift based on the current RMS estimator. We propose a new estimator to improve the zero-amplitude estimation probability during rest. We used 512 rest contraction recordings to validate the probability distribution of rest EMG signal showing that it only has 1.6% difference compared with Gaussian distribution. We also evaluated the percent of zero-valued EMGσ estimates using power domain noise subtraction and our new estimator, matching experimental findings to the theoretic predictions.

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

In the application of cycle-to-cycle control to manufacturing processes, the model of the process reduces to a gain matrix and a pure delay. For a general multiple input – multiple output process, this matrix shows the degree of influence each input has on each output. For a system of high order, determining this gain matrix requires excessive numbers of experiments to be performed, and thus a simplified, but non-ideal form for the gain matrix must be developed. In this paper, the model takes the form of a Gaussian distribution with experimentally determined standard deviation and scaling coefficients. Discrete die sheet metal forming, a multiple input-multiple output process with high dimensionality, is chosen as a test application. Results of the prediction capabilities of the Gaussian model, as well as those of two previously existing models, are presented. It is shown that the Gaussian distribution model does the best job of predicting the output for a given input. The model’s invariance over a set of different formed parts is also presented. However, as shown in the companion paper on cycle-to-cycle control, the errors inherent in this model will cause non-ideal performance of the resulting control system. However, this model appears to be the best form for this problem, given the limit of minimal experimentation. / Singapore-MIT Alliance (SMA)

Julian J. Bussgang. / "March 26, 1952." / Bibliography: p. 14. / Army Signal Corps Contract DA36-039 sc-100, Project no. 8-102B-0. Dept. of the Army Project no. 3-99-10-022.

Thesis (M.S.)--Ohio University, August, 2007. / Title from PDF t.p. Includes bibliographical references.