Covariance models
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Part 2 of ebook "Essentials of time series for financial applications" provides readers with contents including: Chapter 6 - Multivariate GARCH and conditional correlation models; Chapter 7 - Multifactor heteroskedastic models, stochastic volatility; Chapter 8 - Models with breaks, recurrent regime switching, and nonlinearities; Chapter 9 - Markov switching models; Chapter 10 - Realized volatility and covariance;...
189p daonhiennhien 03-07-2024 1 1 Download
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Lecture "Investment: Chapter 3 - Asset pricing models" provide students with content about: Capital asset pricing model; Capital market line; Covariance properties; Covariance matrix;... Please refer to the detailed lecture content!
37p gaupanda031 13-05-2024 4 1 Download
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Prediction of patient survival from tumor molecular ‘-omics’ data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are applied in the context of “high dimension”, as the number p of covariates (gene expressions) greatly exceeds the number n of patients and e of events.
16p vialfrednobel 23-12-2023 3 3 Download
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Part 1 book "Linear models for the prediction of animal breeding values" includes content: Genetic evaluation with different sources of records, genetic covariance between relatives, best linear unbiased prediction of breeding value - univariate models with one random effect, best linear unbiased prediction of breeding value - models with random environmental effects, best linear unbiased prediction of breeding value - models with random environmental effects, methods to reduce the dimension of multivariate models,.... and other contents.
146p oursky06 17-10-2023 3 2 Download
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Continued part 1, part 2 of ebook "Bayesian methods for structural dynamics and civil engineering" provides readers with contents including: bayesian time-domain approach; model updating using eigenvalue–eigenvector measurements; bayesian model class selection; relationship between the hessian and covariance matrix for gaussian random variables; model class selection for regression problems;...
134p hanlinhchi 28-08-2023 10 3 Download
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This paper investigates whether this is the case for 60 Vietnamese provinces for the time-period 1999-2010, using a system-GMM estimator and a Solow growth model augmented with human and physical capital and spatial lag covariates. We report that spatial dependence is a significant determinant of growth and conditional convergence in Vietnamese provinces. We also demonstrate that the rate of convergence decreases as the distancebetween neighbouring provinces increases.
13p tethientran 27-07-2023 5 3 Download
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Part 1 of ebook "Reliability and statistics in geotechnical engineering" provides readers with content including: tntroduction – uncertainty and risk in geotechnical engineering; uncertainty; probability; inference; risk, decisions and judgment;...
314p dieptieuung 19-07-2023 8 4 Download
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To define confounding bias in difference-in-difference studies and compare regression- and matching-based estimators designed to correct bias due to observed confounders. This causal model will then guide the specification of an appropriate analytical model (eg, using regression or matching) that can produce unbiased treatment effect estimates. We emphasize the importance of thoughtful incorporation of covariates to address confounding bias in difference-in-difference studies.
10p vigamora 25-05-2023 5 2 Download
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Lecture Physiological signal processing - Chapter 7: Random signals. This chapter will cover the following: probability models and random variables; jointly distributed random variables; linear estimation; random processes; estimation of mean, variance, and covariance;... Please refer to the chapter for details!
104p chutieubang 06-12-2022 5 3 Download
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Several studies have identified factors influencing infant mortality, but, to the best of knowledge, no studies assessed the factors considering unequal effects on different survival times of infant mortality in Bangladesh. In this study, it was examined how a set of covariate behaves on different quantile survival times related with the infant mortality.
9p viferrari 29-10-2022 2 2 Download
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Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies.
25p viarchimedes 26-01-2022 13 0 Download
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The intention of this paper is to check whether this correlation vanishes if relevant covariates and a sophisticated estimation method are employed. A two-stage Heckman selection model is estimated where the stock of Nobel laureates per capita is regressed on chocolate consumption per capita.
5p lazzaro 30-12-2021 11 0 Download
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These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RSderived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain.
13p lazzaro 30-12-2021 11 1 Download
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The purpose of this paper is to forecast monthly gasoline prices in Jordan by applying Gaussian process regression on monthly prices of two types of gasoline (octane-90 and octane-95) during the period January 2008–December 2019. Accurately predicting gasoline prices have several fiscal policy implications concerning fuel subsidies and taxes. Also, they affect the consumption and the production of decisions. Moreover, they are crucial for designing and analyzing environmental policies.
8p caygaocaolon11 18-04-2021 10 1 Download
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The research objective of the thesis is to consider how the change of covariance matrix factor will affect the results of portfolio selection and through that to find out whether investors have Is it possible to improve portfolio performance by adjusting the covariance matrix in the optimized model with the smallest variance.
55p mmlemmlem_124 22-12-2020 10 3 Download
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RNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences. When quantifying covariance, it evaluates conserved signals of two aligned columns with base-pairing rules.
10p viwyoming2711 16-12-2020 10 1 Download
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Gene set analysis (GSA) of gene expression data can be highly powerful when the biological signal is weak compared to other sources of variability in the data. However, many gene set analysis approaches utilize permutation tests which are not appropriate for complex study designs.
12p vikentucky2711 24-11-2020 8 1 Download
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High-dimensional molecular measurements, e.g. gene expression data, can be linked to clinical time-to-event endpoints by Cox regression models and regularized estimation approaches, such as componentwise boosting, and can incorporate a large number of covariates as well as provide variable selection.
12p vikentucky2711 24-11-2020 10 1 Download
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Construction and validation of a prognostic model for survival data in the clinical domain is still an active field of research. Nevertheless there is no consensus on how to develop routine prognostic tests based on a combination of RT-qPCR biomarkers and clinical or demographic variables.
15p vikentucky2711 24-11-2020 17 2 Download
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Evolutionary variations let us define a set of similar nucleic acid sequences as a family if these different molecules execute a common function. Capturing their sequence variation by using e. g. position specific scoring matrices significantly improves sensitivity of detection tools.
17p vikentucky2711 24-11-2020 9 1 Download