Read e-book online Advanced Sampling Theory with Applications: How Michael ‘ PDF

By Sarjinder Singh (auth.)

ISBN-10: 9400707894

ISBN-13: 9789400707894

ISBN-10: 9401037280

ISBN-13: 9789401037280

This ebook is a multi-purpose record. it may be used as a textual content by way of lecturers, as a reference guide via researchers, and as a realistic consultant by means of statisticians. It covers 1165 references from diversified study journals via virtually 1900 citations throughout 1194 pages, a good number of entire proofs of theorems, vital effects resembling corollaries, and 324 unsolved workouts from a number of study papers. It contains 159 solved, data-based, genuine lifestyles numerical examples in disciplines reminiscent of Agriculture, Demography, Social technology, utilized Economics, Engineering, drugs, and Survey Sampling. those solved examples are very priceless for an realizing of the purposes of complicated sampling idea in our everyday life and in assorted fields of technological know-how. an extra 173 unsolved functional difficulties are given on the finish of the chapters. collage and school professors might locate those priceless while assigning routines to scholars. each one workout offers publicity to a number of whole examine papers for researchers/students.

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Read Online or Download Advanced Sampling Theory with Applications: How Michael ‘ selected’ Amy Volume I PDF

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Additional resources for Advanced Sampling Theory with Applications: How Michael ‘ selected’ Amy Volume I

Example text

In . 52 POINT ESTIMATOR A point estimator endeavours to give the best single estimated value of the parameter. 3 feet. An interval estimator of a population parameter which specifies a range of values bounded by an upper and a lower limit, within which the true value is asserted to lie. 5 feet. 1) where I -a is some fixed probability, the interval between 0, and O2 is called a confidence interval. The assertion that 0 lies in this interval will be true, on the average, in a proportion 1- a of the cases when the assertion is made .

It is defined as: RB=ls(el)I/~MSE(el) where B(e = E(e e and the relative bias l ) l )measurement of the origin al data. ,e" are independently distrib uted random variables with E(ej) = e . I n . 1) v(e). If ej = e(jl is the l is an unbiased estimator of l' ' estimator of e obtained by unit from the samp le of size /I , then such a method of varia nce dropping the estimation are also called Jackknife method of varia nce estimation , and the estimator of variance takes the form i. 44. 2) 11 j=1 .

Sometim es we also consider C y ~Sy /Y. 19 RELATIVE MEAN SQUARE ERROR The relative mean square error is defin ed as the square of the coe fficient of variation Cy and is generally written by RMSE. 1) RMSE = Cy = -=T . y Sometimes it is also denote it by rjJ2 . 2 0. 21 SAMPLE VARIANCE The sample va riance s~ is defined as S 2 y =- 1 /l ( \2 - L Yi - YJ . 1. , are unknown quantities (parameters) and can be denoted by the symbol 8 . , are known after sampling and are called statistic and can be denot ed by iJ .

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Advanced Sampling Theory with Applications: How Michael ‘ selected’ Amy Volume I by Sarjinder Singh (auth.)


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