multivariate general linear models
Tài liệu Modelling the e®ects of air pollution on health using Bayesian Dynamic Generalised Linear Models pdf
... and 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 ... Fahrmeir 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 ... for Multivariate Dynamic Generalized Linear Models Journal of the American Statistical Association 87, 501–509 Fahrmeir, L., W Hennevogl, and K Klemme (1992) Smoothing in dynamic generalized linear...
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Báo cáo khoa học: "Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty" potx
... 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 ... 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, ... 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: "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 ... (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 ... Eisner, 2004) Other work on “generalized probabilistic descent” minimizes a similar objective function but with γ held constant (Katagiri et al., 1998) Although the entropy is generally higher at lower...
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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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Mô hình tuyến tính tổng quát General Linear Model (GLM)
... giới tính (R’S Sex) • Sử dụng phân tích phương sai yếu tố (Two-way anova) Trong SPSS : Analyze /General linear model/ Univariate: cho trường hợp có biến phụ thuộc • Trong mô hình này, sử dụng độ tin...
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Báo cáo hóa học: " Strong consistency of estimators in partially linear models for longitudinal data with mixingdependent structure" pdf
... likelihood for generalized linear models with longitudinal data J Multivar Anal 140, 3445–3456 (2010) 25 Hu, Z, Wang, N, Carroll, RJ: Profile-kernel versus backfitting in the partially linear models ... 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:" Theory and application of general linear image processing" pdf
... CLIP Indeed, using general linear algebra, it is possible to develop entirely new general linear operators and transforms It is even possible to define entirely new general linear operations (addition, ... Although the classical linear image processing (CLIP) approach has played a central role in image processing, it is not necessarily the best and even the right choice General linear image processing ... Theory and application of general linear image processing Jean-Charles Pinoli*1, Guang Deng2 and Karen Panetta3 Ecole Nationale...
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Báo cáo khoa hoc:" Attenuating effects of preferential treatment with Student-t mixed linear models: a simulation study" docx
... 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 of success Use of the ... 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 residuals ... 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
... 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 ... without A, this being representative of linear models used currently for genetic evaluations of first lactation cows in the dairy industry In all three models, the multivariate normal distribution ulo,2 ... distributions is Student’s t, both in its univariate and multivariate forms Several authors [2, 7, 26, 37, 38, 41, 42] have studied linear and non -linear regression problems with Student’s t-distributions,...
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Báo cáo sinh học: "Considerations on measures of precision and connectedness in mixed linear models of genetic evaluation" doc
... compare the three methods, theoretically and with numerical examples based on animal models and sire models some MODELS, NOTATION AND CRITERIA Consider a mixed model with one random factor (and the ... appropriate measure of connectedness Lalo6 (1993) introduced the concept of generalized coefficient of determination (CD), the CD of a linear combination of genetic values, and suggested a new definition ... u and u is multivariate normal, with a null expectation and variance matrix equal to = = = = The distributions of ul û !V(0,C!&dquo;!), respectively The following is a and u - u are multivariate...
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Báo cáo sinh học: "Restricted maximum likelihood estimation of covariances in sparse linear models" potx
... notation for linear stochastic models and mixed model equations, define the REML objective function, and review closed formulas for its gradient and Hessian In sections and we discuss a general setting ... calculation of REML function values and gradients for large and sparse linear stochastic models All our results are completely general, not restricted to animal breeding However, for the formulas ... demonstration case and several large LINEAR STOCHASTIC MODELS AND RESTRICTED LOGLIKELIHOOD Many applications (including those to animal breeding) are based on the gener- alized linear stochastic model with...
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Báo cáo sinh học: "Linear models for joint association and linkage QTL mapping" ppt
... http://www.gsejournal.org/content/41/1/43 mapping" explicitly present two linear models (HaleyKnott type regression for half-sib families and a general mixed model for a general pedigree) and the statistical tests that ... on founders and LA) allow us to construct several linear models considering LD, LA, or both In the next two sections, we will detail two linear models including LD and LA for cases commonly used ... transmission using conditional expectations Ultimately, we come up with linear models for joint association and linkage mapping, which are generalizations of LA mapping Two particular cases will be detailed:...
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Báo cáo sinh học: " Genetic heterogeneity of residual variance estimation of variance components using double hierarchical generalized linear models" ppt
... 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 Stat Soc ... 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, 88:9-25 ... squared residuals with a gamma generalized linear mixed model However, Lee & Nelder [10] have recently developed the framework of double hierarchical generalized linear models (DHGLM) The parameters...
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Báo cáo sinh học: " Carcass conformation and fat cover scores in beef cattle: A comparison of threshold linear models vs grouped data models" docx
... threshold linear models, a threshold log -linear Weibull model, and a grouped data model for the analysis of carcass conformation and fat cover in beef cattle The three threshold linear models were ... homoscedastic and heteroscedastic threshold linear models Given the diversity of models to analyse discrete variables such as CON and FAT scores, comparing these models requires specific tools to test ... Threshold log Linear Weibull Model (TlogLWM) This model assumed proportional (log -linear) effects on CON and FAT scores, instead of the additive effects assumed in the threshold linear models, but...
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Báo cáo sinh học: " Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations" pot
... random effect and the residual variances, respectively The BLUE (Best Linear Unbiased Estimation) of b, denoted b˚, and the BLUP (Best Linear Unbiased ˆ Prediction) of u, denoted u, are the solutions ... corresponding contrast [14] Typically, a given contrast can be written as a linear combination of the breeding values (c u) Hence, for any linear combination c u, we have: CD c u = (cov(c u, c u))2 ˆ · ˆ ... linear regression line of the estimated CD on the true CD The solid line corresponds to the equation y = x Figure True Caco and estimated CD, for the Bazadais dataset The dotted line is the linear...
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Báo cáo sinh học: "Precision and information in linear models of genetic evaluation" ppt
... estimator) of b and following equation system (Henderson, 1984): BLUE (Best linear unbiased M is a projector, orthogonal or, if x is a linear combination of X Precision of the The to the vector prediction ... Contrasts are particular linear combinations x’u, where x is a vector whose elements sum to The precision of any comparison will be evaluated by a precision criterion concerning a linear combination ... use of the CDs of linear combinations Since e can be written as: Some remarks are worth mentioning at this stage: and L’(Z’MZ)L have the same set of eigenvectors, since is a linear function of...
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