linear-in-parameters models
Robust identification for linear in the parameters models
... A large class of nonlinear models and neural networks can be classified as a linear-in-the- parameters model (Harris et al., 2002; Wang and Mendel, 1992). The forward regression approach is an ...
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Sloughing in potatoes induced by tuber density and affected by variety
... ear models of the cooking ...the linear models of the disintegration stage in all groups ...CPEM parameters on density could be better established in the groups with larger density ...3. ...
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Double Generalized Linear Compound Poisson models to Insurance Claims Data
... the parameters, for modelling the claim rate per unit of exposure to change, causing different results in hypothesis tests and discriminant ...the parameters can easily change by more than 10%, making the ...
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Improved estimation of surface biophysical parameters through inversion of linear BRDF models
... Figure 5.1 shows a generally linear relationship between the BPMS-derived volumetric scattering component a and the volumetric kernels, kvo/, particularly for the barley canopy. The relationship is less clear for ...
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Analysis of repeated measurements with missing data
... The models presented so far assume that the mean response is a linear function of the un- known parameters or that a link-function exists such that a linear model for the transformed mean is ...
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Estimation of genetic parameters of reproductive traits in Zandi sheep using linear and threshold models
... genetic parameters in reproductive traits for different sheep breeds using real and simulated ...with linear models estimating genetic parameters and genetic evaluation are ...ability ...
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Estimation for Generalized Linear Models When Covariates Are Subject-specific Parameters in a Mixed Model for Longitudinal Measurements
... (N); (ii) using regression calibration (RC) (Wang et al., 2000, sec. 4.1); (iii) by refined regression calibration, which applies a probit approximation to the logistic to approximate the pseudo-EEE (RR) (Wang et al., ...
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Impedance of Frog Skeletal Muscle Fibers in Various Solutions
... The linear circuit parameters of 140 muscle fibers in nine solutions are determined from phase measurements fitted with three circuit models: the disk model, in which the resistance to r[r] ...
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Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... A linear model with sparse w constructed by solving a linear SVM LP to obtain a subset of variables nonzero-weighted in the linear model; ...the linear SVM LP using “pattern search”; ...
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On a Class of Probability Distributions With Application Using Rainfall Data of Kashmir Valley Bilal Ahmad Bhat 1, N. A. Rather2 , T. A. Rather 3
... Statistical models describe a phenomenon in the form of mathematical ...(called parameters of the ...statistical models are the latest ...of models e.g., Linear models, Non- ...
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Expectation Propagation for Neural Networks with Sparsity-Promoting Priors
... linear models. More generally, our approach can be regarded as a non-linear adaptation of the various EP methods proposed for sparse linear regression ...a linear input layer, and ...
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Application of Hierarchical Linear Models/Linear Mixed effects Models in School Effectiveness Research
... inferences about those specific schools. However, if these schools are regarded as a random sample from a population of schools and the research interest is to make inferences about the variation between schools in ...
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Analysis of Longitudinal Data and Model Selection
... In longitudinal data, missing responses and covariate measurement error are very commonly seen in practice. However ignoring measurement error or omitting missing covariates may produce inconsistent estimators (Little ...
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Estimation of Global Solar Radiation on Horizontal Surface from Sunshine Hours and Other Meteorological Parameters for Calabar, Nigeria
... Multiple linear regression models were developed to estimate the monthly daily Sunshine Hours using four parameters during a period of eleven years (1997 – 2007) for Calabar, Nigeria (Latitude 5 o ...
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Covariance Structures of Linear Models
... We want solutions in exactly this format, no other format will do. Thus one must use full rank generalized inverse. This full rank generalized inverse is always obtained in Experimental Designs theory, by placing, if ...
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Some Modifications to Calculate Regression Coefficients in Multiple Linear Regression
... multiple linear regression model, there are instances where one has to update the regression ...such models as new data become available, by adding one row to the design matrix, the least-squares estimates ...
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Inferential Models for Linear Regression
... various models can be obtained by introducing a prior probability over the model space and a conditional prior on the model parameters, and performing a Markov chain Monte Carlo scan of the model ...for ...
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Sparse nested Markov models with log-linear parameters
... these parameters were truly zero, we would expect the increase to follow a χ 2 -distribution with an ap- propriate number of degrees of freedom; the first two histograms in ...
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Estimating equations approaches to nuisance parameters and outcome-dependent sampling problems for marginal regression models and generalized linear mixed models when outcomes are correlated
... In the previous section, we reviewed three sampling schemes associated with clustered data: proband sampling, stratified sampling, and sampling based on the total. We saw that sampling based on the total can be viewed as ...
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Identification robust inference for endogeneity parameters in linear structural models
... that linear structural models (IV regressions) can be interpreted as regressions with missing ...a linear structural equation (with simultaneity) to a linear regression where all the ...
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