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An efficient algorithm for REML in heteroscedastic regression
 CODEN ???? ISSN 10618600. URL http:/ /lucia.ingentaselect. com/cgibin/linker?ini= asa&reqidx=/cw/asa/10618600/ v11n4/s6/p836. 127 [SN10] [SNB + 01] Stuetzle:2010:GSL Werner Stuetzle and Rebecca
, 2002
"... This paper considers REML (residual or restricted maximum likelihood) estimation for heteroscedastic linear models. An explicit algorithm is given for REMLscoring which yields the REML estimates together with their standard errors and likelihood values. The algorithm includes a LevenbergMarquardt ..."
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Cited by 12 (2 self)
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This paper considers REML (residual or restricted maximum likelihood) estimation for heteroscedastic linear models. An explicit algorithm is given for REMLscoring which yields the REML estimates together with their standard errors and likelihood values. The algorithm includes a Levenberg
Exact and approximate REML for heteroscedastic regression
 Statistical Modelling
, 2001
"... Exact REML for heteroscedastic linear models is compared with a number of approximate REML methods which have been proposed in the literature, especially with the methods proposed by Lee & Nelder (1998) (LN98) and Smyth & Verbyla (1999) (SV99) for simultaneous meandispersion modelling in ge ..."
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Cited by 8 (1 self)
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Exact REML for heteroscedastic linear models is compared with a number of approximate REML methods which have been proposed in the literature, especially with the methods proposed by Lee & Nelder (1998) (LN98) and Smyth & Verbyla (1999) (SV99) for simultaneous meandispersion modelling
Doubly penalized likelihood estimator in heteroscedastic regression
 Statistics and Probability Letter
"... A penalized likelihood estimation procedure is developed for heteroscedastic regression. A distinguishing feature of the new methodology is that it estimates both the mean and variance functions simultaneously without parametric assumption for either. An efficient implementation of the estimating pr ..."
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Cited by 10 (0 self)
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A penalized likelihood estimation procedure is developed for heteroscedastic regression. A distinguishing feature of the new methodology is that it estimates both the mean and variance functions simultaneously without parametric assumption for either. An efficient implementation of the estimating
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
 International Journal of Computer Vision
"... We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix. ..."
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Cited by 93 (7 self)
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We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix.
CONDITIONAL VARIANCE FUNCTION CHECKING IN HETEROSCEDASTIC REGRESSION MODELS
, 2011
"... The regression model has been given a considerable amount of attention and played a significant role in data analysis. The usual assumption in regression analysis is that the variances of the error terms are constant across the data. Occasionally, this assumption of homoscedasticity on the variance ..."
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is violated; and the data generated from real world applications exhibit heteroscedasticity. The practical importance of detecting heteroscedasticity in regression analysis is widely recognized in many applications because efficient inference for the regression function requires unequal variance to be taken
Wavelet Shrinkage Estimates For Heteroscedastic Regression Models
, 1997
"... We study the following heteroscedastic nonparametric regression model: y i = f(t i ) + oe(t i )z i where fz i g is independent identically distributed random noise with z i ¸ N(0; 1) and oe 2 (t) is the variance function. We want to estimate f . We extend Donoho and Johnstone's wavelet shri ..."
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Cited by 5 (0 self)
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We study the following heteroscedastic nonparametric regression model: y i = f(t i ) + oe(t i )z i where fz i g is independent identically distributed random noise with z i ¸ N(0; 1) and oe 2 (t) is the variance function. We want to estimate f . We extend Donoho and Johnstone's wavelet
Conditional Variance Estimation in Heteroscedastic Regression Models
"... First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique t ..."
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Cited by 5 (1 self)
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First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique
Assessing the adequacy of variance function in heteroscedastic regression models
 Biometrics
, 2006
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SLOPE HEURISTICS FOR HETEROSCEDASTIC REGRESSION ON A RANDOM DESIGN
 SUBMITTED TO THE ANNALS OF STATISTICS
, 2008
"... In a recent paper [BM06], Birgé and Massart have introduced the notion of minimal penalty in the context of penalized least squares for Gaussian regression. They have shown that for several model selection problems, simply multiplying by 2 the minimal penalty leads to some (nearly) optimal penalty i ..."
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Cited by 4 (2 self)
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In a recent paper [BM06], Birgé and Massart have introduced the notion of minimal penalty in the context of penalized least squares for Gaussian regression. They have shown that for several model selection problems, simply multiplying by 2 the minimal penalty leads to some (nearly) optimal penalty
Results 1  10
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24,999