000			02552nam a22002057a 4500
005			20250507130553.0
008			250507b |||||||| |||| 00| 0 eng d
020			_a9789819725915
082			_a519.5 _bPAL
100			_aPal, Manisha _923962
245			_aSelected topics in statistical inference: _btheory and applications
260			_bSpringer _aSingapore _c2024
300			_axvii, 142 p.
365			_aEURO _b99.99
500			_aTable of contents: Glimpses of the Book Manisha Pal, Bikas K. Sinha Pages 1-2 Sequential Binomial Estimation Manisha Pal, Bikas K. Sinha Pages 3-43 Use of Additional Resources in Finite Population Inference Manisha Pal, Bikas K. Sinha Pages 45-70 Notion of Sufficiency in Statistical Inference—Theory and Applications Manisha Pal, Bikas K. Sinha Pages 71-92 Estimation of the Size of a Finite Population with Special Features Manisha Pal, Bikas K. Sinha Pages 93-115 Unbiased Estimation of Reliability in Exponential Samples Manisha Pal, Bikas K. Sinha Pages 117-136 [https://link.springer.com/book/10.1007/978-981-97-2592-2]
520			_aThis book focuses exclusively on the domain of parametric inference and that, too, from a reader’s perspective, i.e., covering only point estimation of parameter(s). It covers those topics in parametric inference which need clarity of exposure to students, researchers, and teachers alike; mere statements of theorems and proofs may not always reveal the inner beauty and significance of some aspects of inference. To ensure clarity, the book discusses the following topics at an advanced level—(1) sequential (unbiased) point estimation of ‘p’ and its functions; generalization to trinomial and tetranomial populations; (2) some aspects of the use of additional resources in finite population inference; (3) the concept of sufficiency vis-à-vis the notion of sufficient experiments and comparison of experiments; (4) estimation of the size of a finite population with special features; and (5) unbiased estimation of reliability in exponential samples and other settings. This book provides a platform for thought-provoking, creative, and challenging discussions on a variety of topics in statistical estimation theory, it is also ideal for research methodology course for statistics research scholars, and for clarification of basic ideas in topics discussed at basic/advanced levels. (https://link.springer.com/book/10.1007/978-981-97-2592-2)
650			_aMathematical statistics
700			_aSinha, Bikas K. _923963
942			_cBK _2ddc
999			_c9647 _d9647