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 20 for “"Bias Reduction"”.
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Bias Reduction of Estimates By Bootstrap Method
In general it is desirable to have unbiased estimators for parameters of a probability distribution function. However, there are several estimators which are not unbiased. In this thesis, we show by direct computation that the bias of the bootstrap estimate of μ⁴ can be reduced, where μ is the mean …
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Bias reduction in nonparametric hazard rate estimation
The need of improvement of the bias rate of convergence of traditional nonparametric hazard rate estimators has been widely discussed in the literature. Initiated by recent developments in kernel density estimation we distinguish and extend three popular bias reduction methods to the hazard rate …
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Improving Comprehension Of Capital Sentencing Instructions: A Bias Reduction Approach
… instructions, psycholinguistic rewrites and bias-reduction techniques. Psycholinguistic rewrites of legal instructions have been shown consistently to improve juror comprehension of general legal instructions and instructions used in the sentencing phase of a capital trial, however, there has …
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Three essays on bias, bias reduction and estimation in autoregressive time series models
… one. The first essay derives an approximate bias of the ordinary least squares estimator (OLS) of the autoregressive parameter for series with moderate deviations from a unit root and for a fixed autoregressive coefficient. The result is used to derive the asymptotic distribution of the …
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Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques
… label. We design BELA with strategies to avoid bias that could be introduced through this adaptive partitioning. We evaluate BELA on labeling of four datasets and find that it outperforms existing strategies for adaptive labeling.
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Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images
… decreases in coral cover ranged from 25.3% reduction (Kingman Reef) to 42.7% reduction (Kiritimati Island). Within the Red Sea sites, decrease in coral cover ranged from 3.4% (Umluj) to 13.6% (Al Wajh).</p>
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Evaluation of UIC medical school bias reduction in medicine (BRIM) training on minority medical school application and matriculation
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01
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Validation, trend analysis & bias reduction through satellite fusion of the cloud-top height record from terra MODIS & MISR
… CTH error budget by decomposing the net MISR CTH bias and random errors into constituent errors, such as altitude-dependent MISR wind-retrieval errors and the very first estimate of a stereo-opacity bias (i.e., the retrieval of stereo heights within a depth of the cloud when the extinction near …
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On synchrony and social relations: the role of synchronous multisensory stimulations in self-other merging, social bonding and ingroup-bias reduction
In psicologia sociale, e in particolare negli studi sulle relazioni intime e le relazioni all’interno del gruppo d’appartenenza, è stato osservato come le persone con cui abbiamo dei legami (e.g., partner, amici, membri del nostro gruppo) siano incluse nella rappresentazione del sé. Nella …
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Unsupervised Latent Debiasing of Time-Series Models
… time-series models have been shown to encode the biases from their training corpora into the models themselves. We aim to train unbiased time-series models using existing biased datasets. However, most debiasing techniques rely on explicit labels that encapsulate the bias, such as pairs of words …
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Performance Evaluation of Multiuser Detectors with V-BLAST to MIMO Channel
… the spectral resources over a MIMO channel. A bias reduction technique is considered for multistage parallel interference cancellation receiver on both SISO and MIMO channel. Finally, the effect of channel estimation error and timing delay estimation error is evaluated for MIMO systems with …
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Local Smoothing Methods for the Analysis of Multivariate Complex Data Structures
… It appears that the first concept is useful for bias reduction, while the second one is interesting for robustifcation against outliers in the predictors. As by-products some interesting relations to other mathematical and statistical topics are unveiled, concerning in particular the theorems …
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Modern Econometric Methods for the Analysis of Housing Markets
… literature is that there is a tradeoff between bias and variance linked to the number of matched control observations for each treatment unit. In addition, in the era of Big Data, there is a paucity of research addressing the tradeoffs between inferential accuracy and computational time across …
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Evaluation of a Novel Medical Perspective-Taking Intervention
… al., 2020), stressing the importance of finding bias-reduction interventions that are practical to implement within clinical settings. The present work examined the ability of a novel perspective-taking intervention to increase trust in and improve treatment quality. Affective and cognitive …
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Finite sample properties of the maximum likelihood estimator in continuous time models
… obtain analytical expressions to approximate the bias and variance of the ML estimator in a univariate model with a known mean. We analyze two cases, when the variable of interest is a stock and when it is a flow. We also study the effect of the initial condition by considering both a fixed and a …
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Improving Mixture Cure Modelling of Multiple Molecular Factors in Cancer Prognosis
… maximum likelihood (ML) estimates can be biased and Wald-type confidence intervals may not be valid. This problem can be exacerbated when multiple imputation is used to deal with missing covariate values. Motivated by a cohort study of breast cancer prognosis with incomplete prognostic …
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On some topics of financial theory
… portfolios. Simulations show significant bias of sample statistic from asymptotic counterparts. Bias reduction and related corrections resembling bootstrap are considered. Besides small-sample deviation from asymptotic model, weak results might result from non-existence of approximate …
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Bias Assessment and Reduction in Kernel Smoothing
… evaluated using the Mean Square Error (MSE). Bias and variance are two components of MSE. Kernel methods are known to exhibit varying degrees of bias. Boundary effects and data sparsity issues are two potential problems to watch for. There is a need for a tool to visually assess the potential …
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Enhancing the Reliability of Real-World Evidence and Clinical Decision-Making: Robust, Calibrated, and Uncertainty-Aware Methods for Observational Healthcare Research
… such as model misspecification, systematic biases, and uncertainties in predictions, potentially compromising the reliability and validity of generated evidence. Ensuring reliability is particularly vital for real-world decision-making, where trustworthy evidence must explicitly account for …
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Action Recognition with Knowledge Transfer
… types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor generalization performance. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and …