000			04194nam a22003735i 4500
001			309756
003			MX-SnUAN
005			20160429160322.0
007			cr nn 008mamaa
008			150903s2008 gw | o |||| 0|eng d
020			_a9783790820645 _99783790820645
024	7		_a10.1007/9783790820645 _2doi
035			_avtls000363150
039		9	_a201509030635 _bVLOAD _c201405070339 _dVLOAD _y201402211140 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aShalabh. _eautor _9314330
245	1	0	_aRecent Advances in Linear Models and Related Areas : _bEssays in Honour of Helge Toutenburg / _cby Shalabh, Christian Heumann.
264		1	_aHeidelberg : _bPhysica-Verlag HD, _c2008.
300			_brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aOn the Identification of Trend and Correlation in Temporal and Spatial Regression -- Estimating the Number of Clusters in Logistic Regression Clustering by an Information Theoretic Criterion -- Quasi Score and Corrected Score Estimation in the Polynomial Measurement Error Model -- Estimation and Finite Sample Bias and MSE of FGLS Estimator of Paired Data Model -- Prediction of Finite Population Total in Measurement Error Models -- The Vector Cross Product and 4 × 4 Skew-symmetric Matrices -- Simultaneous Prediction of Actual and Average Values of Response Variable in Replicated Measurement Error Models -- Local Sensitivity in the Inequality Restricted Linear Model -- Boosting Correlation Based Penalization in Generalized Linear Models -- Simultaneous Prediction Based on Shrinkage Estimator -- Finite Mixtures of Generalized Linear Regression Models -- Higher-order Dependence in the General Power ARCH Process and the Role of Power Parameter -- Regression Calibration for Cox Regression Under Heteroscedastic Measurement Error — Determining Risk Factors of Cardiovascular Diseases from Error-prone Nutritional Replication Data -- Homoscedastic Balanced Two-fold Nested Model when the Number of Sub-classes is Large -- QR-Decomposition from the Statistical Point of View -- On Penalized Least-Squares: Its Mean Squared Error and a Quasi-Optimal Weight Ratio -- Optimal Central Composite Designs for Fitting Second Order Response Surface Linear Regression Models -- Does Convergence Really Matter? -- OLS-Based Estimation of the Disturbance Variance Under Spatial Autocorrelation -- Application of Self-Organizing Maps to Detect Population Stratification -- Optimal Designs for Microarray Experiments with Biological and Technical Replicates -- Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions -- Coin Tossing and Spinning – Useful Classroom Experiments for Teaching Statistics -- Linear Models in Credit Risk Modeling.
520			_aThe theory of linear models and regression analysis plays an essential role in the development of methods for the statistical modelling of data. The book presents the most recent developments in the theory and applications of linear models and related areas of active research. The contributions include topics such as boosting, Cox regression models, cluster analysis, design of experiments, feasible generalized least squares, information theory, matrix theory, measurement error models, missing data models, mixture models, panel data models, penalized least squares, prediction, regression calibration, spatial models and time series models. Several contributions illustrate applications in biomedical research, economics, finance, genetic epidemiology and medicine.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aHeumann, Christian. _eautor _9332543
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783790820638
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-7908-2064-5 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c309756 _d309756