Thesis (Ph.D.)--Georgetown University, 2008. / Includes bibliographical references.

Made available in DSpace on 2014-06-11T19:27:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2003-02-21Bitstream added on 2014-06-13T20:08:19Z : No. of bitstreams: 1 yano_fs_me_sjrp.pdf: 492262 bytes, checksum: d370b857c12f73d6431598e15dc347ff (MD5) / Problemas de programação matemática com restriçõesde equilíbrio (MPEC) são problemas de programação não-linear onde as restrições tem uma estrutura análoga condições necessárias de primeira ordem de um problema de otimização com restrições. Em formulações usuais do MPEC todos os pontos factíveis são não-regulares no sentido que não satisfazem a constraint qualification de Mangassarian-Fromovitz. Portanto, todos os pontos factíveis satisfazem a clássica condição necessária de fritz-john. Em princípio, isto poderia causar sérias dificuldades ao aplicarmos algoritmos de programação não-linear ao MPEC. Entretanto, muitos pontos factíveis do MPEC não satisfazem uma condição de otimalidade mais forte que Fritz-John, denominada condição AGP. Esta é a razão na qual em geral os algoritmos de programação não linear são satisfatórios quando aplicados ao MPEC. Nosso objetivo neste trabalho é discutir a aplicabilidade dos algoritmos de programação não-linear ao MPEC.

This paper offers a short report on an Optimality Theoretic analysis of the syllabification of single intervocalic consonants in the Arabic dialect of Sakaka city. This study aimed at investigating how intervocalic consonants of different sonority profiles are treated in the dialect of Sakaka City. Thirty monolingual male participants were recruited voluntarily in this study. Participants’ judgments were elicited using a metalinguistic word blending task with pairs of disyllabic nonwords of the structure ꞌCVCVC + ꞌCVCVC, where stress was on the first syllable only throughout the data. All phonemes involved in this structure are in conformity with Arabic phonotactics. In addition, the intervocalic consonants under examination belonged to four sonority levels; glides ([j] and [w]), liquids ([r] and [l]), nasals ([m] and [n]) and obstruents ([s] and [b]). The low vowel [a] was the only vowel used in this structure. Unlike many works of this nature, ambisyllabicity and word minimality effects were blocked in this complete word task. Although the investigation shed light on several important universal rules of syllabification, sonority profile of intervocalic consonants was the overriding preference in this blending task. That is, glides, liquids and nasals were parsed in coda position by the majority of participants whereas obstruents were parsed in onset position. However, the effects of other universal principles of syllabification such as Maximal Onset Principle and stress placement were minimized. The study concluded that the Split Margin Hierarchy adopted showed a strong preference for coda parse with high sonority consonants and onset parse with low sonority ones, thus adding further support to the abstractness of the syllable as a higher prosodic constituent and the discreteness of phonemes in the human speech stream. Keywords: Arabic dialect, Sakaka city, Optimality Theory, intervocalic consonants, nonwords, ambisyllabicity, minimality effects, Split Margin Hierarchy, sonority, Maximal Onset Principle, stress, syllable, speech stream.

abstract: The majority of research in experimental design has, to date, been focused on designs when there is only one type of response variable under consideration. In a decision-making process, however, relying on only one objective or criterion can lead to oversimplified, sub-optimal decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical during the decision-making process in order to balance the tradeoffs of all potential solutions. Consequently, the problem of constructing a design for an experiment when multiple types of responses are of interest does not have a clear answer, particularly when the response variables have different distributions. Responses with different distributions have different requirements of the design. Computer-generated optimal designs are popular design choices for less standard scenarios where classical designs are not ideal. This work presents a new approach to experimental designs for dual-response systems. The normal, binomial, and Poisson distributions are considered for the potential responses. Using the D-criterion for the linear model and the Bayesian D-criterion for the nonlinear models, a weighted criterion is implemented in a coordinate-exchange algorithm. The designs are evaluated and compared across different weights. The sensitivity of the designs to the priors supplied in the Bayesian D-criterion is explored in the third chapter of this work. The final section of this work presents a method for a decision-making process involving multiple objectives. There are situations where a decision-maker is interested in several optimal solutions, not just one. These types of decision processes fall into one of two scenarios: 1) wanting to identify the best N solutions to accomplish a goal or specific task, or 2) evaluating a decision based on several primary quantitative objectives along with secondary qualitative priorities. Design of experiment selection often involves the second scenario where the goal is to identify several contending solutions using the primary quantitative objectives, and then use the secondary qualitative objectives to guide the final decision. Layered Pareto Fronts can help identify a richer class of contenders to examine more closely. The method is illustrated with a supersaturated screening design example. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2016

abstract: Generalized Linear Models (GLMs) are widely used for modeling responses with non-normal error distributions. When the values of the covariates in such models are controllable, finding an optimal (or at least efficient) design could greatly facilitate the work of collecting and analyzing data. In fact, many theoretical results are obtained on a case-by-case basis, while in other situations, researchers also rely heavily on computational tools for design selection. Three topics are investigated in this dissertation with each one focusing on one type of GLMs. Topic I considers GLMs with factorial effects and one continuous covariate. Factors can have interactions among each other and there is no restriction on the possible values of the continuous covariate. The locally D-optimal design structures for such models are identified and results for obtaining smaller optimal designs using orthogonal arrays (OAs) are presented. Topic II considers GLMs with multiple covariates under the assumptions that all but one covariate are bounded within specified intervals and interaction effects among those bounded covariates may also exist. An explicit formula for D-optimal designs is derived and OA-based smaller D-optimal designs for models with one or two two-factor interactions are also constructed. Topic III considers multiple-covariate logistic models. All covariates are nonnegative and there is no interaction among them. Two types of D-optimal design structures are identified and their global D-optimality is proved using the celebrated equivalence theorem. / Dissertation/Thesis / Doctoral Dissertation Statistics 2018

From the early stage of Optimality Theory (OT) (Prince, Alan and Paul Smolensky (1993): Optimality Theory: Constraint Interaction in Generative Grammar. [ROA: 537-0802: http://roa.rutgers.edu], McCarthy, John J. and Alan Prince (1995). Faithfulness and reduplicative identity. In Jill Beckman, Laura W. Dickey and Suzanne Urbanczyk (eds.) Papers in Optimality Theory. Amherst, MA: GLSA. 249-384), a number of analyses have been proposed to account for vowel harmony in the OT framework. However, because of the diversity of the patterns attested cross-linguistically, no consensus has been reached with regard to the OT treatment of vowel harmony. This, in turn, raises the question whether OT is a viable phonological theory to account for vowel harmony; if a theory is viable, a uniform account of the diverse patterns of vowel harmony should be possible.The main purpose of this thesis is to discuss the application of five different OT approaches to vowel harmony, and to investigate which approach offers the most comprehensive coverage of the diverse vowel harmony patterns. Three approaches are the main focus: feature linking with SPREAD (Padgett, Jaye (2002). Feature classes in phonology. Language 78. 81-110), Agreement-By-Correspondence (ABC) (Walker, Rachel (2009). Similarity-sensitive blocking and transparency in Menominee. Paper presented at the 83rd Annual Meeting of the Linguistic Society of America. San Francisco), and the Span Theory of harmony (McCarthy, John J. (2004). Headed spans and autosegmental spreading. [ROA: 685-0904: http://roa.rutgers.edu]). The applications of these approaches in the following languages are considered: backness and roundness harmony in Turkish and in Yakut (Turkic), and ATR harmony in Pulaar (Niger-Congo). It is demonstrated that both feature linking and ABC analyses are successful in offering a uniform account of the different types of harmony processes observed in these three languages. However, Span Theory turns out to be empirically inadequate when used in the analysis of Pulaar harmony. These results lead to the conclusion that there are two approaches within OT that can offer a uniform account of the vowel harmony processes. This also suggests that OT is viable as a phonological theory.

京都大学 / 0048 / 新制・課程博士 / 博士(文学) / 甲第22169号 / 文博第816号 / 新制||文||684(附属図書館) / 京都大学大学院文学研究科行動文化学専攻 / (主査)教授 定延 利之, 教授 吉田 豊, 准教授 千田 俊太郎 / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DGAM

<div>This thesis is concerned with massive data analysis via robust A-optimally efficient non-uniform subsampling. Motivated by the fact that massive data often contain outliers and that uniform sampling is not efficient, we give numerous sampling distributions by minimizing the sum of the component variances of the subsampling estimate. And these sampling distributions are robust against outliers. Massive data pose two computational bottlenecks. Namely, data exceed a computer’s storage space, and computation requires too long waiting time. The two bottle necks can be simultaneously addressed by selecting a subsample as a surrogate for the full sample and completing the data analysis. We develop our theory in a typical setting for robust linear regression in which the estimating functions are not differentiable. For an arbitrary sampling distribution, we establish consistency for the subsampling estimate for both fixed and growing dimension( as high dimensionality is common in massive data). We prove asymptotic normality for fixed dimension. We discuss the A-optimal scoring method for fast computing. We conduct large simulations to evaluate the numerical performance of our proposed A-optimal sampling distribution. Real data applications are also performed.</div>

Indiana University-Purdue University Indianapolis (IUPUI) / A significant hurdle for analyzing big data is the lack of effective technology and statistical inference methods. A popular approach for analyzing data with large sample is subsampling. Many subsampling probabilities have been introduced in literature (Ma, \emph{et al.}, 2015) for linear model. In this dissertation, we focus on generalized estimating equations (GEE) with big data and derive the asymptotic normality for the estimator without resampling and estimator with resampling. We also give the asymptotic representation of the bias of estimator without resampling and estimator with resampling. we show that bias becomes significant when the data is of high-dimensional. We also present a novel subsampling method called A-optimal which is derived by minimizing the trace of some dispersion matrices (Peng and Tan, 2018). We derive the asymptotic normality of the estimator based on A-optimal subsampling methods. We conduct extensive simulations on large sample data with high dimension to evaluate the performance of our proposed methods using MSE as a criterion. High dimensional data are further investigated and we show through simulations that minimizing the asymptotic variance does not imply minimizing the MSE as bias not negligible. We apply our proposed subsampling method to analyze a real data set, gas sensor data which has more than four millions data points. In both simulations and real data analysis, our A-optimal method outperform the traditional uniform subsampling method.

Indiana University-Purdue University Indianapolis (IUPUI) / Multi-criteria decision making (MCDM) methods have evolved over the past several decades. In today’s world with rapidly growing industries, MCDM has proven to be significant in many application areas. In this study, a decision-making model is devised using reinforcement learning to carry out multi-criteria optimization problems. Learning automata algorithm is used to identify an optimal solution in the presence of single and multiple environments (criteria) using pareto optimality. The application of this model is also discussed, where the model provides an optimal solution to the food, energy, and water systems (FEWS) problem.