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Showing 1 to 20 of 23 for “"Conditional expectation"”.
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Alternating conditional expectation (ACE) applied to classification and recommendation problems
… channel to the application of Alternating Conditional Expectation (ACE) in the problem of optimal regression. The key takeaway of this method is that such problems can be studied in the space of distributions of the data and not the space of outcomes. This geometric framework allows to give …
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Low probability-high consequence considerations in a multiobjective approach to risk management
… consequence accidents by trying to minimize the conditional expected risk given that an accident has occurred. However, if this were the only objective of the model, then poor decisions could result. Therefore, the model formulated is a bicriterion network optimization model that considers …
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Income Inequality Measures and Statistical Properties of Weighted Burr-type and Related Distributions
<p>In this thesis, tail conditional expectation (TCE) in risk analysis, an important measure for right-tail risk, is presented. This value is generally based on the quantile of the loss distribution. Explicit formulas of several tail conditional expectations and inequality measures for Dagum-type …
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A Study in Hybrid Monte Carlo Methods in Computing Derivative Prices
… this thesis as Monte Carlo method that utilizes conditional expectation so that the regular Monte Carlo method and other computational methods can be combined to price financial derivatives. This thesis introduces several hybrid Monte Carlo methods and studies the algorithm and efficiency of …
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Embedding and latent variable models using maximal correlation
… using maximal correlation and the alternating conditional expectation algorithm to construct embeddings one dimensional at a time to maximally preserve the linear correlation in the embedding space. Each dimension is enforced to be orthogonal to all other dimensions to not encode redundant …
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An Exponential Formula for Random Variables Generated by Multiple Brownian Motions
… greater than 1/2.The relationship between the conditional expectation of a random variable (or fractional conditional expectation in the case of fractional Brownian motion)and that variable's Dyson-series like representation is the exponential formula. These results had not yet been extended to …
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Consistency and Convergence of Non-parametric Estimation of Drift and Diffusion Coefficients in SDEs from Long Stationary Time-series
… First, we introduce estimators based on conditional expectation which is motivated by the definition of drift and diffusion coefficients for SDEs. These estimators involve time- and space-discretization parameters for computing discrete analogs of expected values from discretely-sampled …
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Copulas for credit derivative pricing and other applications.
… In the first part of this study, copulabased conditional expectation formulae are described and are applied to small data sets from medicine and hydrology. In the second part of this study we develop a method of improving the estimation of default risk in the context of collateralized debt …
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Parametric Modeling in the Presence of Measurement Error: Monte Carlo Corrected Scores
… the observed data having the property that its conditional expectation given the true data equals a true-data, unbiased score function. Nakamura (1990) gives corrected score functions for special cases, but offers no general solution.It is shown that for a certain class of smooth true-data score …
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Sensitivity Analysis for Non-Ignorable Dropout of Marginal Treatment Effect in Longitudinal Trials for G-Computation Based Estimators
… (LI) estimator can be used to estimate unconditional expectation for longitudinal data from a clinical trial in the presence of dropout. We show that these are analog conditions under which extended linear SWEEP estimator achieves unbiased estimation of the identical parameter in the same …
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Confidence bands for survival functions under semiparametric random censorship models
… based, relying on a parametric model for the conditional expectation of the censoring indicator given the observed minimum, and derives its chief strength from easy access to a good-fitting model among a plethora of choices currently available for binary response data. The substantive …
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Optimal bandwidth selection rule for kernel regression estimator with dependent variables
… valued. Consider the problem of estimating the conditional expectation function, m(x) = E(Y$\sb{\rm t}\vert$ X$\sb{\rm t}$ = x), using (X$\sb1,$Y$\sb1$),$\...$ (X$\sb{\rm n}$,Y$\sb{\rm n}$). (For example, suppose Z$\sb{\rm t}$, t = 0, $\pm$1, $\pm$2,.. is a real valued stationary time series and …
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Poisson regression with Laplace measurement error
… procedure that incorporates the first two conditional moments of the response variable given the observed surrogate, and the weight function is intentionally chosen to avoid the complexity caused by the random denominator and to increase the estimation efficiency. To solve for the …
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Identification and Robustness in Central Banking and Supply Chain
… When bounded uncertainty is passed through a conditional expectation channel, we find that committing not to use a policy tool is sometimes optimal for the central bank. An asset purchasing model and a forward guidance model are examined in depth to illustrate our point. Third, we study a …
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Topics in Tree-Based Methods
… methods, Chapter 3 describes Individual Conditional Expectation (ICE) plots, a methodology for visualizing the model estimated by any supervised learning algorithm. Classical partial dependence plots (PDPs) help visualize the average partial relationship between the predicted response and …
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On estimation and goodness-of-fit testing for the Pareto distribution
… of order statistics while the second is based on conditional expectation. We derive the asymptotic properties of the tests based on the first of these characterisations. Extensive Monte Carlo studies are included in order to examine the finite sample power performance of the proposed tests and it …
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Essays on Empirical Asset Pricing
… Chapter One provides an estimator for the conditional expectation function using a partially misspecified model. The estimator automatically detects the dimensions along which the model quality is good (poor). The estimator is always consistent, and its rate of convergence improves toward …
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Methods to Improve Fairness and Accuracy in Machine Learning, with Applications to Financial Algorithms
… it establishes a notion of the “direction” of a conditional expectation function that motivates the design of accurate new classifiers.
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An ensemble based approach for drug sensitivity prediction
… output responses. Experimental results show that conditional expectation based on multivariate probability distribution and knowledge of the response of a correlated drug can be utilized to considerably improve prediction accuracy.
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From Points to Objects: Statistical Inference Beyond Euclidean Spaces
… and identically distributed sample the conditional expectation of a random object given a vector of real numbers. To that end, we present an adaptation of random forest together with an approximate tree construction algorithm. Our approximation algorithm allows to perform regression in …
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