Global ETD Search
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 251 for “"Linear regression models"”.
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Imputing Genotypes Using Regularized Generalized Linear Regression Models
… Here, the use of a regularized generalized linear model, denoted GLMNET is proposed to impute missing genotypes. The method aimed to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly multicollinearity among predictors. The …
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Efficient Estimation of Linear Regression Models With Autocorrelated Errors
Made available in DSpace on 2014-12-14T06:06:04Z (GMT). No. of bitstreams: 1 7804091.pdf: 5452448 bytes, checksum: 10c8e65fdd1bc2bcffec86dfdd8aa565 (MD5) Previous issue date: 1977
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Identifying outliers and influential observations in general linear regression models
… the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In …
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In-situ prediction on sensor networks using distributed multiple linear regression models
… enabling model-driven computation where complex models are replaced by in-situ sensing and communication. These prediction models utilize low-computation, low-communication, and distributed algorithms suited to autonomous operation and multiple applications. We achieve this through development of …
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Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia's Hospital
Fuzzy linear regression analysis has become popular among researchers and standard model in analysing data vagueness phenomena. These models were represented by five statistical models such as multiple linear regression, fuzzy linear regression (Tanaka), fuzzy linear regression (Ni), extended fuzzy …
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Two applications of U-Statistic type processes to detecting failures in risk models and structural breaks in linear regression models
… is concerned with detecting failures in Risk Models and in detecting structural breaks in linear regression models. By applying Theorem 2.1 of Szyszkowicz on U-statistic type process, a number of weak convergence results regarding three weighted partial sum processes are established. It is …
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Subsampling methods for robust inference in regression models
… the subsampling methods for robust estimation in regression models when there are possible outliers in the data. Two basic proposals of the subsampling method are investigated. The main idea is to identify good data points through fitting the model to randomly chosen subsamples. Subsamples …
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Predicting the effectiveness of commute reduction plans using neural networks
… ability of neural networks compared to linear regression models in calculating the change in vehicle trip rate (VTR) at a given worksite over a year long travel plan period. Using a Los Angeles area dataset (n = 3439), linear regression and neural network models were constructed and …
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Vitamin D, Calcium and Blood Pressure in the U.S.
… and Examination Survey (NHANES 2001-2006). Linear regression models were used to determine the adjusted association of 25(OH)D categories with blood pressure. An interaction term between 25(OH)D and calcium intake was fitted in the fully adjusted regression model. All analyses accounted for …
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A methodological study and forecasting of room occupancy trend in Las Vegas
… forecasting; The analytical model includes log-linear regression models and "other" extrapolation forecasting models for model parameter estimation and optimization; The model is applied to Las Vegas and the U.S. lodging occupancy data from 1973 through 1990 and is used to forecast Las Vegas and …
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Clustering Analysis of Zernike Coefficients From High Order Aberration Patients
… coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the …
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Spatiotemporal Drivers of Municipal Water Consumption
… slices (1990, 2000, and 2010). Global multiple linear regression models and measures of global and local spatial association were combined to determine which landscape characteristics significantly influenced per capita municipal water consumption at the county scale, whether or not the …
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Post-mortem organ weights at a South African mortuary
… subsamples. After assumption testing, multiple linear regression models are built, including interaction terms. Factors influencing organ weights are discussed and results compared to selected studies. Ideal organ weight reference ranges are proposed, based on 95% inclusion data from decedents …
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Detection of Outliers and Influential Observations in Regression Models
<p>Observations arising from a linear regression model, lead one to believe that a particular observation or a set of observations are aberrant from the rest of the data. These may arise in several ways: for example, from incorrect or faulty measurements or by gross errors in either response or …
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Assessment of Individual Differences in Online Social Networks Using Machine Learning
… using conventional dimensionality reduction and linear regression models. Here I develop a supervised dimensionality reduction method capable of intelligently selecting only useful parts of data for the relevant prediction at hand which also does not lose variance when eliminating redundancies. …
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An explorative study of linear vs non-linear hedonic pricing of second-hand cars in South Africa
… Using a hedonic pricing approach to develop four linear and four non-linear regression models the study analyses a comprehensive dataset of 4,386 second-hand vehicles collected from the Cars.co.za website in 2018 and focuses on eight key vehicle attributes, namely age, mileage, region (province of …
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Hypothesis testing procedures for non-nested regression models
… families of hypotheses. Two hypothesized models are said to be non-nested when one model is neither a restricted case nor a limiting approximation of the other. These non-nested hypotheses cannot be tested using conventional likelihood ratio procedures. In recent years, however, several …
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The Potential Effects of Exercise-Induced Cortisol Release on Executive Functioning in Preadolescents
… salivary samples for the analysis of cortisol. Linear Regression models were used to assess significance of covariates. Generalized linear models were used to assess significance of changes in each dependent variable against time, condition, time*condition and change in cortisol. Tukey’s HSD …
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A Comparison of Natural Gas Spot Price Linear Regression Forecasting Models
… EIA model are compared to the results of other linear regression models.
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Analyzing the relationship between the Gross Domestic Product (GDP) of Lesotho and manufacturing: 1997to 2007
… of Statistics in Lesotho. Simple and multiple linear regression models techniques are used to analyze the relationship between the GDP of Lesotho and the GDP of manufacturing. The secondary data is analyzed using Statistical Packages for Social Sciences (SPSS) and Excel. The major finding is …
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