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Proceedings of the 29th ACM International Conference on Multimedia, 2021
In this work, we study the problem of separating the global camera motion and the local dynamic motion from an optical flow. Previous methods either estimate global motions by a parametric model, such as a homography, or estimate both of them by an optical flow field. However, none of these methods can directly estimate global and local motions through
Yuchen Yang +5 more
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In this work, we study the problem of separating the global camera motion and the local dynamic motion from an optical flow. Previous methods either estimate global motions by a parametric model, such as a homography, or estimate both of them by an optical flow field. However, none of these methods can directly estimate global and local motions through
Yuchen Yang +5 more
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2021
GLMs with a binomial distribution are designed for the analysis of binomial counts (how many times something occurred relative to the total number of possible times it could have occurred). A logistic link function constrains predictions to be above zero and below the maximum using the S-shaped logistic curve.
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GLMs with a binomial distribution are designed for the analysis of binomial counts (how many times something occurred relative to the total number of possible times it could have occurred). A logistic link function constrains predictions to be above zero and below the maximum using the S-shaped logistic curve.
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GLM and joint GLM techniques in hydrogeology: an illustration
International Journal of Hydrology Science and Technology, 2012In regression models with positive observations, estimation is often based on either the log-normal or the gamma model. Generalised linear models and joint generalised linear models are appropriate for analysing positive data with constant and non-constant variance, respectively.
Rabindra Nath Das, Jinseog Kim
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Dir‐GLM: A Bayesian GLM With Data‐Driven Reference Distribution
Statistics in MedicineABSTRACTThe recently developed semi‐parametric generalized linear model (SPGLM) offers more flexibility as compared to the classical GLM by including the baseline or reference distribution of the response as an additional parameter in the model. However, some inference summaries are not easily generated under existing maximum‐likelihood‐based inference
Entejar Alam +2 more
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2010
This chapter initially discusses topics like deviances, hypothesis testing and estimation of the dispersion parameter. The interpretation of deviances as measures of goodness-of-fit is highlighted. Next comes asymptotic normality of the estimators, the construction of confidence intervals and the role played by the Fisher information.
Esbjörn Ohlsson, Björn Johansson
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This chapter initially discusses topics like deviances, hypothesis testing and estimation of the dispersion parameter. The interpretation of deviances as measures of goodness-of-fit is highlighted. Next comes asymptotic normality of the estimators, the construction of confidence intervals and the role played by the Fisher information.
Esbjörn Ohlsson, Björn Johansson
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2021
Binomial GLMs can also be used to analyse binary data as a special case, with some minor differences introduced into the analysis by the constrained nature of the binary data.
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Binomial GLMs can also be used to analyse binary data as a special case, with some minor differences introduced into the analysis by the constrained nature of the binary data.
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2000
As we did in the previous chapter, we give a number of applications of the major results obtained in Chapter 2. We do so for the General Linear Structural Econometric Model (GLSEM), an important topic for many fields but especially for econometrics.
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As we did in the previous chapter, we give a number of applications of the major results obtained in Chapter 2. We do so for the General Linear Structural Econometric Model (GLSEM), an important topic for many fields but especially for econometrics.
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Functional programming for GLMs
1989The statistician of the 21st century will have been educated in a modern computing environment and will expect statistical modelling software to reflect recent advances in computer technology. Existing statistical software and the current languages used for statistical analysis are based on somewhat old-fashioned computing concepts.
Michael Clarke +3 more
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2017
This chapter contains some extensions of the multiple linear regression model. See Definition 1.1 for the 1D regression model , sufficient predictor (SP = h(x)), estimated sufficient predictor (\(ESP =\hat{ h}(\mathbf{x})\)), generalized linear model (GLM), and the generalized additive model (GAM).
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This chapter contains some extensions of the multiple linear regression model. See Definition 1.1 for the 1D regression model , sufficient predictor (SP = h(x)), estimated sufficient predictor (\(ESP =\hat{ h}(\mathbf{x})\)), generalized linear model (GLM), and the generalized additive model (GAM).
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Generalized Linear Models (GLMs)
2019Generalized Linear Models are widely known under their famous acronym GLMs. Today, GLMs are recognized as an industry standard for pricing personal lines and small commercial lines of insurance business. This chapter reviews the GLM methodology with a special emphasis to insurance problems.
Michel Denuit +2 more
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