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 166 for “"Misclassification"”.
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Topics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination.
… We investigate ordered differential response misclassification in a logistic regression model and provide an adjustment for it using a conditional prior structure. We examine a parametric Bayesian Weibull proportional hazards model with ordered covariate misclassification and provide an …
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Wald Confidence Intervals for a Single Poisson Parameter and Binomial Misclassification Parameter When the Data is Subject to Misclassification
… error-free data and error-prone data subject to misclassification in the form of false-negative and false-positive counts. We present maximum likelihood estimators (MLEs), Fisher's Information, and Wald statistics for Poisson rate parameter and the two misclassification parameters. Next, we …
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Improving Ultra-Wideband Localization by Detecting Radio Misclassification
<p>The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern …
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Statistical methods for outcome misclassification adjustment in causal inference and spatial classification.
Outcome misclassification occurs when a categorical variable is incorrectly assigned to a group due to an imperfect diagnostic device. The failure to account for outcome misclassification results in biased estimation and inaccurate predictions. This dissertation focuses on correcting outcome …
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SENSITIVITY ANALYSIS – THE EFFECTS OF GLASGOW OUTCOME SCALE MISCLASSIFICATION ON TRAUMATIC BRAIN INJURY CLINICAL TRIALS
I. EFFECTS OF GLASGOW OUTCOME SCALE MISCLASSIFICATION ON TRAUMATIC BRAIN INJURY CLINICAL TRIALS The Glasgow Outcome Scale (GOS) is the primary endpoint for efficacy analysis of clinical trials in traumatic brain injury (TBI). Accurate and consistent assessment of outcome after TBI is essential to …
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Administrative Coding of Type 1 and Type 2 Diabetes: Assessment of Validity and Implications of Coding Practices ob Outcome Evaluation
… is a difficult task. Objectives: to evaluate misclassification of diabetes status and type, examine contributors to classification uncertainty and explore how difficult cases are perceived by health professionals. Methods: A chart review was performed to validate coding of diabetes type in two …
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The comparative study of model-based and appearance based gait recognition for leave bag behind
… KNN (K nearest Neighbour) based on accuracy and misclassification rates (MER) metrics. The experiment results show that the accuracy and misclassification rate (MER) of Appearance-based approaches obtained is 93.66% and 6.33% respectively tested on SVM classifier then the accuracy and …
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Network meta-analysis with rare events and misclassified response.
… to as non-sampling error. Errors such as misclassification, measurement error, and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data but also incorporate sensitivity …
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Count regression models with a misclassified binary covariate : a Bayesian approach.
Mismeasurment, and specifically misclassification, are inevitable in a variety of regression applications. Fallible measurement methods are often used when infallible methods are either expensive or not available. Ignoring mismeasurement will result in biased estimates for the associated regression …
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Integrated-likelihood-ratio confidence intervals obtained from data via a double-sampling scenario.
… data, business data, and other types of data, misclassification is often present and can cause estimators to be biased, thus leading to incorrect conclusions. Tenenbein (1970) has provided a double-sampling scheme to correct for misclassification through the use of an infallible data set that …
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Bayesian and maximum likelihood methods for some two-segment generalized linear models.
… Poisson and logistic regression models, misclassification in the response is well known to cause attenuation of key parameters and other difficulties. How misclassification effects estimation of a CP in such models has not been studied. In this research, we consider the effect of …
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Essays on the impact of education on misclassified civic outcomes: studies of Italy and the UK
… topics and over-report civic opinions (i.e., misclassification). This approach allows me to ascertain the extent to which the causal effect of schooling on the civic indicators is either genuine or is driven mainly by endogeneity and a systematic misreporting by educational levels. I also …
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Elliptical cost-sensitive decision tree algorithm - ECSDT
… impact of the costs associated with different misclassification errors, continues to be one of the major challenging areas for data mining and machine learning. The literature reviews in this area show that most of the cost-sensitive algorithms that have been developed during the last decade …
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Bayesian approach to inference and variable selection for misclassified and under-reported response models.
… In particular, we consider non-differential misclassification in logistic regression and non-differential under-reporting in Poisson regression. Differential misclassification and differential under-reporting are also addressed but in less detail. We then develop a Bayesian approach to select …
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Incorporating game theory with soft sets for better decision making
… different parameters, resulting in a decrease in misclassification error in an environment involving uncertainty. Furthermore, the extent of the decrease can be fine-tuned by adjusting the ratio between the cost for misclassification error and the cost for undecided error. Based on the user’s …
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On Identifying Critical Nuggets Of Information During Classification Task
… metrics - classification accuracy and misclassification costs. Experimental results helped validate that critical nuggets can assist in improving classification accuracies in real world data sets when compared with other standalone classification algorithms. The improvements in accuracy …
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Prostate cancer recognition in ultrasound images
… recognize cancer in cancerous ultrasound images. Misclassification occurs when cancerous tissue is classified as non-cancerous and noncancerous tissue is classified as cancerous. Occurrences of misclassification have been observed and investigated. (Abstract shortened by UMI.).
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Using a Bayesian model for bankruptcy prediction : a comparative approach
… on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- …
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Linear discriminant analysis
… split into two components, one for each type of misclassification, i.e. classification of an individual into population I given it arose from population II, and classification of it into II given it arose from I. Similarly, the second level leads to consideration of expected errors and the third …
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