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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 479 for “"principal components"”.
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Modified principal components regression
… on the least squares estimator, the procedure of principal components regression deletes those components corresponding to the small latent roots of X'X. Then we regress <u>y</u> on the retained components using ordinary least squares. When principal components regression is used as an alternative …
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Components and principles of streaming principal components
Principal Component Analysis (PCA) is a fundamental pillar of modern data pipelines, but its traditional implementation is woefully inadequate for modern data problems. In this work we present our contributions to the field of streaming principal component analysis---research that adds critical …
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Macroeconomic Forecasting: Statistically Adequate, Temporal Principal Components
… this dissertation is to expand upon the use of Principal Component Analysis (PCA) in macroeconomic forecasting, particularly in cases where traditional principal components fail to account for all of the systematic information making up common macroeconomic and financial indicators. At the …
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Hedging Interest-Rate Options Using Principal Components Analysis
… securities. As an alternative, we consider using principal components analysis (PCA) to condense most of the variability in the market rates into a much smaller number of risk factors, called the principal components. One can then construct a hedging portfolio so as to make the portfolio immune to …
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Longitudinal principal components analysis for binary and continuous data
… part of this thesis, we propose a longitudinal principal component analysis (LPCA) using a random-effects eigen-decomposition, where the eigen-decomposition utilizes longitudinal information over time to model time-varying eigenvalues and eigenvectors of the corresponding covariance matrices. …
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Fractional principal components regression: a general approach to biased estimators
… model. Though the ridge estimator and the principal components estimator have been widely used for such problems, it should be noted that their performances in terms of mean square error are dependent upon the orientation of the unknown parameter vector and the magnitude of σ². By defining …
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Modeling daily electricity load curve using cubic splines and functional principal components
… future month. The last approach uses functional principal components to model the daily electricity load profile for each day as a linear combination of three eigenfunctions, with the coefficients of the day-specific linear combinations modeled as univariate time series using transfer functions. …
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Early Forest Fire Detection Using Texture Analysis of Principal Components from Multispectral Video
… variance of a smoke plume is captured through Principal Component Analysis (PCA) where the Multispectral-Multitemporal PCA is performed on a sequence of video frames simultaneously. The presence of a plume existing in one of the higher order principal components is determined by the texture of …
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Inter-finger coordination in robot hands via mechanical implementation of principal components analysis
… collected with a dataglove, and analyzed using principal components analysis to determine the postural synergies. The synergies are then mechanically hardwired into the driving mechanism, resulting in a concept dubbed eigenpostures.
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P300-Based BCI Performance Prediction through Examination of Paradigm Manipulations and Principal Components Analysis.
… correlation with the user's performance. These components may provide a fast and reliable metric to indicate whether the BCI system will work for a given individual.</p>
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Early Forest Fire Detection Using Texture, Blob Threshold, and Motion Analysis of Principal Components
… of the infrared (IR) video data, and (2) Principal Component Analysis of visible spectrum video data followed by motion analysis and adaptive intensity threshold. The two schemes presented are tailored to detect the fire core, and the smoke plume, respectively.</p> <p>Cooled Midwave …
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Developing Criteria for Extracting Principal Components and Assessing Multiple Significance Tests in Knowledge Discovery Applications
… research is the determination of the number of components to retain in principal components analysis. This research establishes regression, asymptotic theory, and neural network approaches for estimating mean and 95th percentile eigenvalues for implementing Horn's parallel analysis procedure for …
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Unconfined compressive strength prediction using drilling parameters and analyzing feature importance through principal components analysis
… consists of a data processing method called Principal Component Analysis (PCA) to indicate the importance of each parameter by quantifying their variance contribution. Random Forest machine learning algorithm is utilized to build a regression model to estimate UCS. The regression model …
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Atmospheric temperature profile estimation from infrared and microwave spectral radiance observations using principal components analysis
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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The importance of selecting the optimal number of principal components for fault detection using principal component analysis
… increases plant availability and utilization. Principal component analysis is one of the statistical techniques that are used for fault detection. Determination of the number of PCs to be retained plays a big role in detecting a fault using the PCA technique. In this dissertation focus has been …
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An Asymptotic Principal Components Performance Measurement for Mutual Funds With Emphasis on Fixed Income Funds Relative to Balanced and Stock Funds
This study attempts first to analyze the performance of bond, balanced, and stock mutual funds relative to the market and each other. It builds a performance measurement model based upon the Connor-Korajczyk Jensen-like measure. This model enables one to avoid some of the problems associated with …
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POSITIVE PEER CULTURE PROBLEM LABELS AND JUVENILE DELINQUENCY: AN EXPLORATORY PRINCIPAL COMPONENTS ANALYSIS AND ORDINARY LEAST SQUARES REGRESSION ANALYSIS OF LOW SELF-IMAGE, INCONSIDERATE OF OTHERS, AND INCONSIDERATE OF SELF
<p>Using the National Longitudinal Study of Adolescent Health (Waves I and II) this study examines the utility of three problem labels (low self-image, inconsiderate of others, and inconsiderate of self) put forth in Harry Vorrath and Larry Brendtro’s (1985) book, Positive Peer Culture (PPC). PPC …
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POSITIVE PEER CULTURE PROBLEM LABELS AND JUVENILE DELINQUENCY: AN EXPLORATORY PRINCIPAL COMPONENTS ANALYSIS AND ORDINARY LEAST SQUARES REGRESSION ANALYSIS OF LOW SELF-IMAGE, INCONSIDERATE OF OTHERS, AND INCONSIDERATE OF SELF
<p>Using the National Longitudinal Study of Adolescent Health (Waves I and II) this study examines the utility of three problem labels (low self-image, inconsiderate of others, and inconsiderate of self) put forth in Harry Vorrath and Larry Brendtro’s (1985) book, Positive Peer Culture (PPC). PPC …
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Investigation of factor rotation routines in principal component analysis of stock returns
… routines that will produce uncorrelated rotated principal components for a dataset of stock returns, in an attempt to identify the macroeconomic factors that best explain the variability among risk-adjusted stock returns on the Johannesburg Stock Exchange. An alternative to the more traditional …
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Three Essays on Macroeconomic and Econometric Analysis of Business Cycle
… factor model. This paper suggests a modified principal components regression method: using the Chicago Federal Reserve Bank National Activity Index (CFNAI) and local economic indicators, local and national factors are extracted by applying the partitioned regression and the principal …
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