000			03646nam a2200397 i 4500
005			20250918234747.0
008			140602s2013 flua b 001 0 eng
020			_a9781439858028 _q(hardback) _cRM322.42
020			_a1439858020 _q(hardback)
039		9	_a201508240909 _brosli _c201508051458 _dhamudah _c201502171033 _dbaiti _y06-02-2014 _zhamudah
040			_aDLC _beng _erda _cDLC _dYDX _dBTCTA _dUKMGB _dOCLCO _dYDXCP _dVRC _dOCLCF _dSTF _dUKM
090			_aQA276.8.H554
090			_aQA276.8 _b.H554
100	1		_aHilbe, Joseph M., _d1944-
245	1	0	_aMethods of statistical model estimation / _cJoseph M. Hilbe, Jet Propulsion Laboratory, California Institute of Technology, USA, and Arizona State Univeristy, USA, Andrew P. Robinson, ACERA & Department of Mathematics and Statistics, The University.
264		1	_aBoca Raton : _bCRC Press, _c2013.
264		4	_c©2013.
300			_axii, 243 pages : _billustrations ; _c25 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_a'Preface Methods of Statistical Model Estimation has been written to develop a particular pragmatic viewpoint of statistical modelling. Our goal has been to try to demonstrate the unity that underpins statistical parameter estimation for a wide range of models. We have sought to represent the techniques and tenets of statistical modelling using executable computer code. Our choice does not preclude the use of explanatory text, equations, or occasional pseudo-code. However, we have written computer code that is motivated by pedagogic considerations first and foremost. An example is in the development of a single function to compute deviance residuals in Chapter 4. We defer the details to Section 4.7, but mention here that deviance residuals are an important model diagnostic tool for GLMs. Each distribution in the exponential family has its own deviance residual, defined by the likelihood. Many statistical books will present tables of equations for computing each of these residuals. Rather than develop a unique function for each distribution, we prefer to present a single function that calls the likelihood appropriately itself. This single function replaces five or six, and in so doing, demonstrates the unity that underpins GLM. Of course, the code is less efficient and less stable than a direct representation of the equations would be, but our goal is clarity rather than speed or stability. This book also provides guidelines to enable statisticians and researchers from across disciplines to more easily program their own statistical models using R. R, more than any other statistical application, is driven by the contributions of researchers who have developed scripts, functions, and complete packages for the use of others in the general research community'-- _cProvided by publisher.
650		0	_aEstimation theory.
700	1		_aRobinson, Andrew _q(Andrew P.)
907			_a.b15918440 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQA276.8.H554
914			_avtls003561318
990			_abety
991			_aSains Forensik
991			_aFakulti Sains dan Teknologi
998			_at _b2014-02-06 _cm _da _feng _gflu _y0 _z.b15918440
999			_c571348 _d571348