Purpose: With the growing importance of performance measurement and management, this exploratory study intends to examine the practices of maintenance managers with regards to maintenance measures, as used in their organizations. In this process, the study attempts to uncover the relevant maintenance performance dimensions from the perspectives of the surveyed managers. In addition, the mediating effect of information availability on the main performance measures utilization is studied. Design/methodology/approach: The research at hand is survey-based. It utilizes the responses of a sample of ninety-five (95) experienced maintenance managers to identify the most relevant maintenance performance measures. Factor analysis is then utilized to uncover the important dimensions of performance, as seen by the respondents. Additionally, using the Partial Least Squares method, several models were studied. Findings: The findings of this exploratory research appear to suggest that maintenance managers are beginning to broaden their perspective with regard to performance management. While machine and plant-related performance measures are still emphasized, maintenance managers are slowly moving toward a wider organizational orientation. While the manufacturing organizations are becoming more and more customer-oriented open systems, the maintenance function of these organizations is still, for the most part, operating under the semi-open system orientation. Overall, it appears that an emerging maintenance strategy is slowly taking shape. Research limitations/implications: For the most part, performance measures and measurement related to maintenance have not received enough attention from researchers. Therefore, the literature dealing with the different facets of performance in maintenance has not been forthcoming. The study attempts to fill this apparent gap in the literature. This is important, as maintenance managers are being asked to contribute to the achievement of the competitive strategies of their organizations. Therefore, they must quickly learn how to view maintenance from a coherent strategic organizational perspective. Such a perspective should help in integrating the maintenance, resources, capabilities, and technical know-how in order to serve the strategic goal of their organization. The research at hand is limited to a sample from Portugal. Therefore, the results and conclusions must be interpreted accordingly. Practical implications: As maintenance managers struggle to move from a machine-orientation to a more organizational-wide strategic orientation, they are often left with many questions and few answers. This study attempts to bring this problem to the spotlight so that it can receive more systematic empirical and practical research. In this context, the role of maintenance managers in the process of organizational strategy formulation should be examined. Originality/value: The study presented in this article has practical, as well as theoretical contributions. It deals with an area of performance measurement, which so far has been relatively ignored. It uses a system orientation (closed vs open), in addition to the strategic orientation (single vs multi-faceted strategy) in order to shed some light on the need to have consistency between the nature of the system and its strategic objective.

In this reviewwe discuss methodology to ascertain the amount of information in given data sets with respect to determination of model parameters with desired levels of uncertainty.We do this in the context of least squares (ordinary,weighted, iterative reweightedweighted or "generalized", etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in the biological and environmental sciences.

In this note we explain the use of the Akiake Information Criterion and its related model comparison indices (usually derived for maximum likelihood estimator inverse problem formulations) in the context of least squares (ordinary, weighted, iterative weighted or “generalized”, etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in biology.

Background – This study is an attempt to explore the relationship between microcredit and the socio-economic empowerment of women in rural Bangladesh. Microcredit is simply the extension of a small amount of collateral-free institutional loans to jointly liable poor group members to generate employment and income enhancing activities. As it is too difficult for poor members to get loan from the formal credit institutions, Grameen Bank (GB) or other Non-Government Organizations (NGOs) provide small loans to vulnerable groups of the society by which they are expected to empower over his counterparts. Research questions – RQ1: How does micro-credit affect different indicators of women empowerment in the rural areas of Bangladesh? RQ2– Is the impact different from the male counterparts in the sample households? Purpose – This study is an effort to find the impact of microcredit on a number of indicators of women’s empowerment in the rural areas in Bangladesh. Methodology – Quantitative Regression Techniques such as Ordinary Least Square (OLS) and Instrumental Variable (IV) method have been applied to get the relationship between microcredit and women empowerment. Conclusion – Applying nationally representative cross-section survey data, Bangladesh Integrated Household Survey (BIHS) 2015, this thesis is intended to find the causal linkage between microcredit and women empowerment’s with different dimensions of women’s decisions are taken as empowerment indicators: production, resources, income, leadership, savings and time. The analysis has been conducted at the household level. The study assumes that women empowerment is endogenous. After controlling for endogeneity in the estimation by using an instrumental variable (IV) ‘distance to the market’ this study finds a significant relationship between microcredit and different dimensions of women’s empowerment. Participation in the microcredit program is found to be significant in explaining some of the outcome indicators of empowerment for the sampled households.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Bibliography : leaves 49-51. / M.S.

No description available.

Accompanying the advances in computer technology is an increase collection of high dimensional data in many scientific and social studies. Sufficient dimension reduction (SDR) is a statistical method that enable us to reduce the dimension ofpredictors without loss of regression information. In this dissertation, we introduce principal asymmetric least squares (PALS) as a unified framework for linear and nonlinear sufficient dimension reduction. Classical methods such as sliced inverse regression (Li, 1991) and principal support vector machines (Li, Artemiou and Li, 2011) often do not perform well in the presence of heteroscedastic error, while our proposal addresses this limitation by synthesizing different expectile levels. Through extensive numerical studies, we demonstrate the superior performance of PALS in terms of both computation time and estimation accuracy. For the asymptotic analysis of PALS for linear sufficient dimension reduction, we develop new tools to compute the derivative of an expectation of a non-Lipschitz function. PALS is not designed to handle symmetric link function between the response and the predictors. As a remedy, we develop expectile-assisted inverse regression estimation (EA-IRE) as a unified framework for moment-based inverse regression. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimension reduction based on random projections of the regression expectiles. Several popular inverse regression methods in the literature including slice inverse regression, slice average variance estimation, and directional regression are extended under this general framework. The proposed expectile-assisted methods outperform existing moment-based dimension reduction methods in both numerical studies and an analysis of the Big Mac data. / Statistics

Distribution load flow and state estimation are two important functions in distribution energy management systems (DEMS) and advanced distribution automation (ADA) systems. Distribution load flow analysis is a tool which helps to analyze the status of a power distribution system under steady-state operating conditions. In this research, an effective and comprehensive load flow algorithm is developed to extensively incorporate the distribution system components. Distribution system state estimation is a mathematical procedure which aims to estimate the operating states of a power distribution system by utilizing the information collected from available measurement devices in real-time. An efficient and computationally effective state estimation algorithm adapting the weighted-least-squares (WLS) method has been developed in this research. Both the developed algorithms are tested on different I testeeders and the results obtained are justified.

Hyperspectral imagery is often associated with high storage and transmission costs. Dimensionality reduction aims to reduce the time and space complexity of hyperspectral imagery by projecting data into a low-dimensional space such that all the important information in the data is preserved. Dimensionality-reduction methods based on transforms are widely used and give a data-dependent representation that is unfortunately costly to compute. Recently, there has been a growing interest in data-independent representations for dimensionality reduction; of particular prominence are random projections which are attractive due to their computational efficiency and simplicity of implementation. This dissertation concentrates on exploring the realm of computationally fast and efficient random projections by considering projections based on a random Hadamard matrix. These Hadamard-based projections are offered as an alternative to more widely used random projections based on dense Gaussian matrices. Such Hadamard matrices are then coupled with a fast singular value decomposition in order to implement a two-stage dimensionality reduction that marries the computational benefits of the data-independent random projection to the structure-capturing capability of the data-dependent singular value transform. Finally, random projections are applied in conjunction with nonnegative least squares to provide a computationally lightweight methodology for the well-known spectral-unmixing problem. Overall, it is seen that random projections offer a computationally efficient framework for dimensionality reduction that permits hyperspectral-analysis tasks such as unmixing and classification to be conducted in a lower-dimensional space without sacrificing analysis performance while reducing computational costs significantly.

DNA methylation plays a key role in disease analysis, especially for studies that compare known large scale differences in CpG sites, such as cancer/normal studies or between-tissues studies. However, before any analysis can be done, data normalization and preprocessing of methylation data are required. A useful data preprocessing pipeline for large scale comparisons is Functional Normalization (FunNorm), (Fortin et al., 2014) implemented in the minfi package in R. In FunNorm, the univariate quantiles of the methylated and unmethylated signal values in the raw data are used to preprocess the data. However, although FunNorm has been shown to outperform other preprocessing and data normalization processes for these types of studies, it does not account for the correlation between the methylated and unmethylated signals into account; the focus of this paper is to improve upon FunNorm by taking this correlation into account. The concept of a bivariate quantile is used in this study as an attempt to take the correlation between the methylated and unmethylated signals into consideration. From the bivariate quantiles found, the partial least squares method is then used on these quantiles in this preprocessing. The raw datasets used for this research were collected from the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI) website. The results from this preprocessing algorithm were then compared and contrasted to the results from FunNorm. Drawbacks, limitations and future research are then discussed. / Thesis / Master of Science (MSc)