000 02390cam a2200361 i 4500
001 19946
003 OSt
005 20241031132853.0
008 190614s2020 enka b 001 0 eng d
020 _a9780128158623
_q(pbk.)
040 _aUKMGB
_beng
_cUKMGB
_erda
_dOCLCO
_dOCLCF
_dYDX
_dFIE
_dSFB
_dBDX
_dDLC
_dIQ-MoCLU
082 0 4 _a519.536
_223
_bF619
245 0 0 _aFlexible Bayesian regression modelling /
_cedited by Yanan Fan, David Nott, Michael S. Smith, Jean-Luc Dortet-Bernadet.
264 1 _aLondon, United Kingdom ;
_aSan Diego, CA, United States :
_bAcademic Press,
_c[2020]
300 _axiv, 288 pages ;
_c23 cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
504 _aIncludes bibliographical references and index.
520 _aFlexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine.--
650 0 _aRegression analysis
_xMathematical models.
650 0 _aBayesian statistical decision theory.
650 7 _aBayesian statistical decision theory.
_2fast
650 7 _aRegression analysis
_xMathematical models.
_2fast
700 1 _aFan, Y.
_q(Yanan),
_eeditor.
700 1 _aNott, David,
_eeditor.
700 1 _aSmith, Michael S.,
_eeditor.
700 1 _aDortet-Bernadet, Jean-Luc,
_eeditor.
776 0 8 _iebook version :
_z9780128158630
910 _aSAJA
942 _2ddc
_cBK
999 _c19946
_d19946