Introduction to Mathematical StatisticsAn exceptionally clear and impeccably accurate presentation of statistical applications and more advanced theory. Included is a chapter on the distribution of functions of random variables as well as an excellent chapter on sufficient statistics. More modern technology is used in considering limiting distributions, making the presentations more clear and uniform. |
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Page 110
Here let f(xu x2, . . . , x„) be the joint p.d.f. of the n random variables Xu X2, . . . , X„,
just as before. Now, however, let us take any group of k < n of these random
variables and let us find the joint p.d.f. of them. This joint p.d.f. is called the
marginal ...
Here let f(xu x2, . . . , x„) be the joint p.d.f. of the n random variables Xu X2, . . . , X„,
just as before. Now, however, let us take any group of k < n of these random
variables and let us find the joint p.d.f. of them. This joint p.d.f. is called the
marginal ...
Page 167
Let X have a p.d.f. = \, x = 1, 2, 3, zero elsewhere. Find the p.d.f. of Y = 2X+ 1. 4.18
. If /(x„ x2) = (§)x ' + x^)2 -x2, (x„ x2) = (0, 0), (0, 1), (1, 0), (1, 1), zero elsewhere, is
the joint p.d.f. of X, and X2, find the joint p.d.f. of y, = Xx - X2 and Y2 = A', + AV ...
Let X have a p.d.f. = \, x = 1, 2, 3, zero elsewhere. Find the p.d.f. of Y = 2X+ 1. 4.18
. If /(x„ x2) = (§)x ' + x^)2 -x2, (x„ x2) = (0, 0), (0, 1), (1, 0), (1, 1), zero elsewhere, is
the joint p.d.f. of X, and X2, find the joint p.d.f. of y, = Xx - X2 and Y2 = A', + AV ...
Page 202
Let Z = (T, + y3)/2 be the midrange of the sample. Find the p.d.f. of Z. 4.66. Let Yx
< Y2 denote the order statistics of a random sample of size 2 from 7V(0, a2). (a)
Show that E(Yx) = -a\Jn. Hint: Evaluate E(Y\) by using the joint p.d.f. of Yx and Y2,
...
Let Z = (T, + y3)/2 be the midrange of the sample. Find the p.d.f. of Z. 4.66. Let Yx
< Y2 denote the order statistics of a random sample of size 2 from 7V(0, a2). (a)
Show that E(Yx) = -a\Jn. Hint: Evaluate E(Y\) by using the joint p.d.f. of Yx and Y2,
...
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Accordingly approximate best critical region chi-square distribution complete sufficient statistic conditional p.d.f. conditional probability confidence interval Consider continuous type converges in probability correlation coefficient critical region defined degrees of freedom denote a random depend upon 9 discrete type distribution function F(x distribution with mean distribution with p.d.f. distribution with parameters equation estimator of 9 Example Exercise F-distribution gamma distribution given H0 is true hypothesis H0 independent random variables integral joint p.d.f. Let the random Let Xu X2 limiting distribution marginal p.d.f. matrix moment-generating function order statistics p.d.f. of Xu percent confidence interval Poisson distribution positive integer probability density functions probability set function quadratic form random experiment random sample random variables Xx reject H0 respectively sample space Section Show significance level simple hypothesis statistic for 9 sufficient statistic testing H0 theorem unbiased estimator variance a2 Xx and X2 Yu Y2 zero elsewhere