000			03279nam a22004455i 4500
001			978-81-322-0763-4
003			DE-He213
005			20140220082927.0
007			cr nn 008mamaa
008			121009s2013 ii | s |||| 0|eng d
020			_a9788132207634 _9978-81-322-0763-4
024	7		_a10.1007/978-81-322-0763-4 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aRajarshi, M. B. _eauthor.
245	1	0	_aStatistical Inference for Discrete Time Stochastic Processes _h[electronic resource] / _cby M. B. Rajarshi.
264		1	_aIndia : _bSpringer India : _bImprint: Springer, _c2013.
300			_aXI, 113 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Statistics, _x2191-544X
505	0		_aCAN Estimators from dependent observations -- Markov chains and their extensions -- Non-Gaussian ARMA models -- Estimating Functions -- Estimation of joint densities and conditional expectation -- Bootstrap and other resampling procedures -- Index.
520			_aThis work is an overview of statistical inference in stationary, discrete time stochastic processes.  Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aStatistics, general.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9788132207627
830		0	_aSpringerBriefs in Statistics, _x2191-544X
856	4	0	_uhttp://dx.doi.org/10.1007/978-81-322-0763-4
912			_aZDB-2-SMA
999			_c99115 _d99115