analysis hierarchical linear models
Báo cáo sinh học: " Genetic heterogeneity of residual variance estimation of variance components using double hierarchical generalized linear models" ppt
... 88:1156-1161 Lee Y, Nelder JA: Double hierarchical generalized linear models (with discussion) Appl Stat 2006, 55:139-185 Lee Y, Nelder JA: Hierarchical generalized linear models (with Discussion) J R ... approximation [19,21,22] Linear mixed models and HGLM Here, a linear mixed model with homoskedastic residuals is considered Lee & Nelder [11] have shown that REML estimates for linear mixed models can be ... hglm: A package for fitting hierarchical generalized linear models R Journal (accepted) 2010 Breslow NE, Clayton DG: Approximate inference in generalized linear mixed models J Am Stat Ass 1993,...
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Báo cáo sinh học: "Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs CS Wang" pdf
... moderately sized data sets Hence, it should be useful in the analysis of experimental data Iberian methods pig / genetic parameters / linear model / Bayesian / Gibbs sampler Résumé - Analyse bayésienne ... practice To simplify, attention may be restricted to linear predictors Henderson (1963, 1973) and Henderson et al (1959) developed the best linear unbiased prediction (BLUP), which removed the ... the first moments of the distributions BLUP is the linear function of the data that minimizes mean square error of prediction in the class of linear unbiased predictors Bulmer (1980), Gianola...
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From interval analysis to taylor models an overview lohner
... the flow Thus it is possible to work with nonlinear boundary curves, including non-convex enclosure sets for crescent-shaped or twisted flows For nonlinear ODEs, this increased flexibility in admissible ... interval methods and Taylor model methods with a simple, but illuminating nonlinear example Example 3: Taylor model method for nonlinear model IVP We consider the IVP u = v, u(0) = + a, v = u2 , v(0) ... Perspectives of Enclosure Methods, pages 201–217 Springer, Wien, 2001 [19] K Makino Rigorous analysis of nonlinear motion in particle accelerators PhD thesis, Michigan State University, 1998 [20]...
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Tài liệu Modelling the e®ects of air pollution on health using Bayesian Dynamic Generalised Linear Models pdf
... method of analysis uses Poisson linear or additive models In this paper we use a Bayesian dynamic generalised linear model (DGLM) to estimate this relationship, which allows the standard linear ... and Tutz (2001)) to analyse air pollution and health data Dynamic generalised linear models extend generalised linear models by allowing the regression parameters to evolve over time via an autoregressive ... suggests areas for future work Bayesian Dynamic generalised linear models A Bayesian dynamic generalised linear model extends a generalised linear model by allowing a subset of the regression parameters...
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Báo cáo khoa học: "Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty" potx
... for L1-regularized loglinear models Experimental results are presented in Section Some related work is discussed in Section Section gives some concluding remarks Log -Linear Models In this section, ... algorithm pact and accurate models much more quickly than the OWL-QN algorithm This paper is organized as follows Section provides a general description of log -linear models used in NLP Section ... remarks Log -Linear Models In this section, we briefly describe log -linear models used in NLP tasks and L1 regularization A log -linear model defines the following probabilistic distribution over...
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Báo cáo khoa học: "Decision detection using hierarchical graphical models" docx
... features Hierarchical graphical models Although the results just discussed showed graphical models are better than SVMs for detecting decision dialogue acts (Bui et al., 2009), two-level graphical models ... et al., 2001) and outperform maximum entropy Markov models and HMMs However, the graphical models used in these applications are mainly non -hierarchical, including those in Bui et al (2009) Only ... 2009) and activity recognition (Bui et al., 2002) Undirected graphical models (UGMs) are also valuable for building probabilistic models for segmenting and labeling sequence data Conditional 308 http://corpus.amiproject.org/...
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Báo cáo khoa học: "Beyond Log-Linear Models: Boosted Minimum Error Rate Training for N-best Re-ranking" docx
... is more serious for log -linear models of around 10 features and focus on it in this work To truly achieve the benefits of re-ranking in MT, one must go beyond the log -linear model The reranker ... difference of BLEU The final classifier f T can be seen as a voting procedure among multiple log -linear models generated by MERT The weighted vote for hypotheses in an N-best list xi is represented ... outperforms MERT by 0.8 points on Eval Related Work Various methods are used to optimize log -linear models in re-ranking (Shen et al., 2004; Venugopal et al., 2005; Smith and Eisner, 2006) Although...
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Báo cáo khoa học: "Contrastive Estimation: Training Log-Linear Models on Unlabeled Data∗" potx
... syntactic model should make it so Log -Linear Models We have not yet specified the form of our probabilistic model, only that it is parameterized by θ ∈ Rn Log -linear models, which we will show are a ... (upper box) and unsupervised (lower box) estimation with log -linear models in terms of Eq where Ai ⊂ Bi (for each i) For log -linear models this is simply (x,y)∈Ai i (x,y)∈Bi u x, y | θ (6) u x, ... most models were the best on their own criterion, and among unsupervised models, L ENGTH performed best on the CL criterion 360 RB∗ →ADV) To take better advantage of the power of loglinear models specifically,...
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Báo cáo khoa học: "Parsing the WSJ using CCG and Log-Linear Models" pptx
... dependencies Log -linear models (also known as Maximum Entropy models) are popular in NLP because of the ease with which discriminating features can be included in the model Log -linear models have ... Gaussian prior for smoothing maximum entropy models Technical report, Carnegie Mellon University, Pittsburgh, PA Stephen Clark and James R Curran 2003 Log -linear models for wide-coverage CCG parsing ... structure (1), the objective function is as follows: L (Λ) = L(Λ) − G(Λ) (4) m = log Log -Linear Parsing Models eλ f (ω ) ZS = n PΛ (π j |S j ) − j=1 i=1 m d∈∆(π j ) log = j=1 eλ f (ω) − log j=1...
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Báo cáo khoa học: "Minimum Risk Annealing for Training Log-Linear Models∗" doc
... metrics for natural language tasks from two broadly applicable classes: linear and nonlinear A linear metric is a sum (or other linear combination) of the loss or gain on individual sentences Accuracy—in ... yi with the min-loss analysis in the hypothesis set; if multiple analyses tie (2) yi Since small changes in θ either not change the best analysis or else push a different analysis to the top, ... (e.g., the reference translation(s)) to the hypothesis set during training For such models, γ merely aids the nonlinear optimizer in its search, by making it easier to scale all of θ at once 789...
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Báo cáo khoa học: "Log-linear Models for Word Alignment" ppt
... coverage as features Our experiments show that log -linear models significantly outperform IBM translation models We begin by describing log -linear models for word alignment The design of feature ... search algorithm for log -linear models We will follow with our experimental results and conclusion and close with a discussion of possible future directions Log -linear Models Formally, we use ... words, our loglinear models share GIZA++ with the same parame464 ters apart from POS transition probability table and bilingual dictionary Table compares the results of our log -linear models with...
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matrix analysis & applied linear algebra - carl d meyer
... called backward error analysis, as opposed to forward analysis in which one tries to answer the question, “How close will a computed solution be to the exact solution?” Backward analysis has proven ... contemporary theory and applications of linear algebra to university students studying mathematics, engineering, or applied science at the postcalculus level Because linear algebra is usually encountered ... Does “Applied” Mean? Most people agree that linear algebra is at the heart of applied science, but there are divergent views concerning what “applied linear algebra” really means; the academician’s...
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Báo cáo hóa học: " Strong consistency of estimators in partially linear models for longitudinal data with mixingdependent structure" pdf
... for such models If g(⋅) is unknown but there are no repeated measurements, that is m1 = ⋅ ⋅ ⋅ = mn = 1, the models (1.1) are reduced to non-longitudinal partially linear regression models, which ... partially linear regression models Our results can also be extended to the case of (xT , tij ) being random The interested readers can consider the ij work In addition, we consider partially linear models ... doi:10.2307/2289218 Heckman, N: Spline smoothing in a partly linear models J R Stat Soc B 48, 244–248 (1986) Speckman, P: Kernel smoothing in partial linear models J R Stat Soc B 50, 413–436 (1988) Robinson,...
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báo cáo hóa học:" Statistical analysis of linear spatial holes estimators in cognitive radio" ppt
... Statistical analysis of linear spatial holes estimators in cognitive radio Mohammad Kazemi∗ , Mehrdad Ardebilipour ... bound (CRLB) In this article, the performance of cognitive RSS-WLS algorithm which is an important linear spatial hole estimation algorithm in CR systems has been analyzed by obtaining the closed ... the field of simultaneous estimation of location and power, especially in CR networks In [2], a linear RSS algorithm based on weighted least square (WLS) method is introduced and analyzed which...
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Chương 9: Modeling What Could Go Wrong: Risk Analysis on Goal Models docx
... Chap.9: Risk Analysis on Goal Models © 2009 John Wiley and Sons Risk analysis can be anchored on goal models www.wileyeurope com/college/van lamsweerde Chap.9: Risk Analysis on Goal Models © 2009 ... Chap.9: Risk Analysis on Goal Models Cost Maintainability Deadline Variability Cost Device interaction Software interoperability Convenience © 2009 John Wiley and Sons Risk analysis on goal models: ... know about the domain ! Obstacle analysis may help elicit relevant domain properties www.wileyeurope com/college/van lamsweerde Chap.9: Risk Analysis on Goal Models © 2009 John Wiley and Sons...
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Báo cáo khoa hoc:" Attenuating effects of preferential treatment with Student-t mixed linear models: a simulation study" docx
... impact of outliers on data analysis Many authors have employed statistical models with t-distributed residuals [4, 12, 13, 25, 31] in linear and non -linear regression models, with varying degrees ... effects or hierarchical models is relatively recent !1, 2, 5, 6, 22-24, 26, 30] Our objective was to assess frequentist properties of Bayesian point estimators obtained from mixed linear models where ... variance) Table I Average treatment treatment as a ) h (a 2.3 Statistical models and computations Three linear statistical models were compared, both with and without preferential treatment incorporated...
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Báo cáo khoa hoc:" Mixed effects linear models with t-distributions for quantitative" docx
... been advocated for the analysis of quantitative genetic data with mixed linear models [8, 9, 34, 39, 40], and the Bayesian solutions suggested employ Gaussian sampling models as well as normal ... assumption to make, as then the machinery of mixed effects linear models can be exploited An appealing alternative is to fit linear models with robust distributions for the errors and for the ... Gianola [36] in a more comprehensive evaluation of the the models DISCUSSION A Bayesian method for analysis of mixed effects linear models with t-distributed random effects, with emphasis on...
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Báo cáo khoa hoc:" On the use analysis of animal models in the of selection experiments" pdf
... of analysis of selection experiments, essentially based on least-square estimators (see [8]), new methods based on mixed models were developed from then Moving from sire models to animal models ... the beginning of the experiment (analysis I), and then having separate groups of consecutive generations analysed independently (analysis II) As shown in table 11, analysis I indicates that, as ... mixed models include information components based on the selection pressure applied The estimator of R is then a function including S, which is not the case in the standard methods of analysis...
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