• inference
  • Inference regarding multiple structural changes in linear models with endogenous regressors ," Centre for Growth and Business Cycle Research Discussion Paper Series 125, Economics, The Univeristy of Manchester. (repec.org)
  • Inference regarding multiple structural changes in linear models with endogenous regressors ," Journal of Econometrics , Elsevier, vol. 170(2), pages 281-302. (repec.org)
  • dependence
  • Modelling Asymmetric Exchange Rate Dependence ," International Economic Review , Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May. (repec.org)
  • Modelling Asymmetric Exchange Rate Dependence ," Working Papers wp04-04, Warwick Business School, Finance Group. (repec.org)
  • parameters
  • We analyze the identification and estimation of parameters β satisfying the incomplete linear moment restrictions E(z T (x β−y)) = E(z T u(z)) where z is a set of instruments and u(z) an unknown bounded scalar function. (repec.org)
  • Estimate parameters and test hypotheses in generalized linear models by means of statistical software. (uib.no)
  • It shows that identification restrictions are linear in the structural parameters and are therefore easy to use. (nber.org)
  • They proposed an iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. (wikipedia.org)
  • In order to make the model identifiable, we need to impose constraints on parameters. (wikipedia.org)
  • The central algorithm adopted is the iteratively reweighted least squares method, for maximum likelihood estimation of usually all the model parameters. (wikipedia.org)
  • Techniques for estimating the LNP model parameters include: moment-based techniques, such as the spike-triggered average or spike-triggered covariance with information-maximization or maximum likelihood techniques. (wikipedia.org)
  • polynomial
  • These models approximate a neuron's input-output characteristics using a polynomial expansion analogous to the Taylor series, but do not explicitly specify the spike-generation process. (wikipedia.org)
  • estimation
  • Describe numerical procedures for estimation in generalized linear models. (uib.no)
  • Estimating Nonlinear Dynamic Models Using Least Absolute Error Estimation ," Econometric Theory , Cambridge University Press, vol. 7(01), pages 46-68, March. (repec.org)
  • Instrumental Variable Estimation Of A Threshold Model ," Econometric Theory , Cambridge University Press, vol. 20(05), pages 813-843, October. (repec.org)
  • During estimation, rather than using weighted least squares during IRLS, one uses generalized least squares to handle the correlation between the M linear predictors. (wikipedia.org)
  • Greven, S. (2014), "A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models", Electronic Journal of Statistics, 8: 201-225, doi:10.1214/14-EJS881 Pinheiro, J. C. (wikipedia.org)
  • Zener
  • The standard linear solid (SLS) model, also known as the Zener model, is a method of modeling the behavior of a viscoelastic material using a linear combination of springs and dashpots to represent elastic and viscous components, respectively. (wikipedia.org)
  • The model was first developed by Zener. (wikipedia.org)
  • The SLS model (green) has two different datasets, left c0=1990 m/s, Qc=100 and τr−1=2π100 right c0=1985 m/s, Qc=84.71 and τr−1=6.75x100 Fig.1.Attenuation - Kolsky model and Zener model (Standard Linear Solid) Ursin B. and Toverud T. 2002 Comparison of seismic dispersion and attenuation models. (wikipedia.org)
  • Working Papers
  • Set Identified Linear Models ," TSE Working Papers 09-090, Toulouse School of Economics (TSE). (repec.org)
  • Set Identified Linear Models ," IDEI Working Papers 494, Institut d'Économie Industrielle (IDEI), Toulouse. (repec.org)
  • Set identified linear models ," CeMMAP working papers CWP13/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. (repec.org)
  • Bounds in Competing Risks Models and the War on Cancer ," CAM Working Papers 2004-08, University of Copenhagen. (repec.org)
  • Bounds in Competing Risks Models and the War on Cancer ," NBER Working Papers 10963, National Bureau of Economic Research, Inc. (repec.org)
  • Partial Identification in Monotone Binary Models : Discrete Regressors and Interval Data ," Working Papers 2004-11, Center for Research in Economics and Statistics. (repec.org)
  • Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data ," IDEI Working Papers 280, Institut d'Économie Industrielle (IDEI), Toulouse, revised Jan 2005. (repec.org)
  • Econometric Evaluation of Asset Pricing Models ," NBER Technical Working Papers 0145, National Bureau of Economic Research, Inc. (repec.org)
  • Estimating and Testing Multiple Structural Changes in Models with Endogenous Regressors ," Boston University - Department of Economics - Working Papers Series wp2008-017, Boston University - Department of Economics. (repec.org)
  • predicts
  • Imagine, for example, a model that predicts the likelihood of a given person going to the beach as a function of temperature. (wikipedia.org)
  • SLS is the simplest model that predicts both phenomena. (wikipedia.org)
  • typically
  • Furthermore, the sum of terms in a linear model typically can have very large ranges encompassing very negative and very positive values. (oracle.com)
  • assumes
  • This course assumes a solid knowledge of Calculus (I, II, III), linear algebra (Math. (utah.edu)
  • The model assumes that the long-term, biological damage caused by ionizing radiation (essentially the cancer risk) is directly proportional to the dose. (wikipedia.org)
  • It opposes two competing schools of thought: the threshold model, which assumes that very small exposures are harmless, and the radiation hormesis model, which claims that radiation at very small doses can be beneficial. (wikipedia.org)
  • predictors
  • The algorithm can build and score quality models that use a virtually limitless number of predictors (attributes). (oracle.com)
  • The best regression models are those in which the predictors correlate highly with the target, but there is very little correlation between the predictors themselves. (oracle.com)
  • The generalized functional linear model (GFLM) is an extension of the generalized linear model (GLM) that allows one to regress univariate responses of various types (continuous or discrete) on functional predictors, which are mostly random trajectories generated by a square-integrable stochastic processes. (wikipedia.org)
  • restrictions
  • The advantage of linear models and their restrictions include computational simplicity, an interpretable model form, and the ability to compute certain diagnostic information about the quality of the fit. (oracle.com)
  • Generalized linear models relax these restrictions, which are often violated in practice. (oracle.com)
  • responses
  • The LNP model is generally implicit when using reverse correlation or the spike-triggered average to characterize neural responses with white-noise stimuli. (wikipedia.org)
  • Methods
  • Applied Regression Analysis, Linear Models, and Related Methods. (southampton.ac.uk)
  • Various approximate methods have been developed, but none has good properties for all possible models and data sets (e.g. ungrouped binary data are particularly problematic). (wikipedia.org)
  • The above results point therefore to a failure of naive perturbation theory in describing correctly the physical behavior of the O(n)-symmetric model above two dimensions, and to the need for more sophisticated non-perturbative methods such as the lattice formulation. (wikipedia.org)
  • binary
  • Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data ," Review of Economic Studies , Oxford University Press, vol. 75(3), pages 835-864. (repec.org)
  • Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data ," Post-Print halshs-00754272, HAL. (repec.org)
  • Topics include an introduction to the basic two-level model for continuous outcomes, assessment of fit, checking model assumptions, single and multiparameter hypothesis testing, the extension to three-level models, and nonlinear models for binary outcomes. (umich.edu)
  • linearly
  • Generalized linear models cover all these situations by allowing for response variables that have arbitrary distributions (rather than simply normal distributions), and for an arbitrary function of the response variable (the link function) to vary linearly with the predicted values (rather than assuming that the response itself must vary linearly). (wikipedia.org)
  • assumptions
  • The challenge in developing models of this type involves assessing the extent to which the assumptions are met. (oracle.com)
  • theory
  • To introduce the theory and apply a wide range of statistical models. (southampton.ac.uk)
  • Understand the foundation theory of Generalised Linear Models. (southampton.ac.uk)
  • In each case, the designation "linear" is used to identify a subclass of models for which substantial reduction in the complexity of the related statistical theory is possible. (wikipedia.org)
  • Plane Answers to Complex Questions: The Theory of Linear Models (Third ed. (wikipedia.org)
  • Current models of innovation deriving from approaches such as Actor-Network Theory, Social shaping of technology and social learning (e.g. (wikipedia.org)
  • This model proved to be relevant in string theory where the two-dimensional manifold is named worldsheet. (wikipedia.org)
  • The existence of such a fixed point is relevant, as it grants, at this order of perturbation theory, that conformal invariance is not lost due to quantum corrections, so that the quantum field theory of this model is sensible (renormalizable). (wikipedia.org)
  • An alternative to the LNP model for neural characterization is the Volterra kernel or Wiener kernel series expansion, which arises in classical nonlinear systems-identification theory. (wikipedia.org)
  • values
  • the minimising values βj are linear functions of the random errors εi which makes it relatively easy to determine the statistical properties of the estimated values of βj. (wikipedia.org)
  • Thus the contexts where these models are useful or realistic often depends on the range of the values being modelled. (wikipedia.org)
  • estimates
  • What the model estimates. (socialresearchmethods.net)
  • Pending any definitive answer to these questions and the precautionary principle, the model is sometimes used to quantify the cancerous effect of collective doses of low-level radioactive contaminations, even though it estimates a positive number of excess deaths at levels that would have had zero deaths, or saved lives, in the two other models. (wikipedia.org)
  • Econometric Society
  • Bounds in Competing Risks Models and the War on Cancer ," Econometrica , Econometric Society, vol. 74(6), pages 1675-1698, November. (repec.org)
  • Estimating and Testing Linear Models with Multiple Structural Changes ," Econometrica , Econometric Society, vol. 66(1), pages 47-78, January. (repec.org)
  • analysis
  • GLMMs provide a broad range of models for the analysis of grouped data, since the differences between groups can be modelled as a random effect. (wikipedia.org)
  • These models are useful in the analysis of many kinds of data, including longitudinal data. (wikipedia.org)
  • Graph
  • The term may specifically be used for: A log-linear plot or graph, which is a type of semi-log plot. (wikipedia.org)
  • presents the graph for the Kolsky model (blue) with two datasets (left and right)and same data - attenuation with cr=2000 m/s, Qr=100 and wr=2π100 Hz. (wikipedia.org)
  • slope
  • The main thing that encode does in this case is % build a model containing an coefficient for the intercept and the slope. (mathworks.com)
  • Statistics
  • Transparency is also a key feature: model details describe key characteristics of the coefficients, and global details provide high-level statistics. (oracle.com)
  • Once built the same model can be analyzed in many ways including least-squares regression, fit and lack-of-fit statistics, ANOVA (or ANACOVA), MANOVA (or MANACOVA) This tutorial will use several examples to show how to build different types of models. (mathworks.com)
  • all test statistics use the SSD matrix from the largest model considered as the (generalized) denominator. (psu.edu)
  • In statistics, the term linear model is used in different ways according to the context. (wikipedia.org)
  • In statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. (wikipedia.org)
  • prediction
  • SQL function to obtain the confidence bounds of a model prediction. (oracle.com)
  • As an example, a prediction model might predict that 10 degree temperature decrease would lead to 1,000 fewer people visiting the beach is unlikely to generalize well over both small beaches (e.g. those where the expected attendance was 50 at a particular temperature) and large beaches (e.g. those where the expected attendance was 10,000 at a low temperature). (wikipedia.org)
  • The problem with this kind of prediction model would imply a temperature drop of 10 degrees would lead to 1,000 fewer people visiting the beach, a beach whose expected attendance was 50 at a higher temperature would now be predicted to have the impossible attendance value of −950. (wikipedia.org)