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10 Questions with Key - Statistics Method II - Midterm | STAT 632, Exams of Statistics

Material Type: Exam; Professor: Hart; Class: STAT METH II BAYES MODEL; Subject: STATISTICS; University: Texas A&M University; Term: Unknown 1989;

Typology: Exams

Pre 2010

Uploaded on 02/10/2009

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STAT 632-600
October
29, 2008 Midterm Exam
Instructions: Write all your answers
on the test paper provided. Circle what you think is
the correct answer
on multiple choice
questions,
and be spectfic on short-answer
quest'ions.
Point values are indicated in parentheses.
Good luck!
1. (8) Let )/ be the set of all possible
observations.
A class
P of priors is conjugate
for a
qiven set of likelihoods
.F if
((u)J"(
.ld eP for alln
€P and all
a ey.
(b) "( .lA) e P for some n e P and all A e y.
(c) r e P implies that n('lg) is aproper
distribution
for each
A ey.
(d) the likelihood
is ploportional
to some
member of P for each
g ey.
2. (8) The Jeffreys
noninformative prior is
(a) sometimes
improper. .
(b) proportional to the squa,re
root of the determinant of the information matrix.
(pI derived from an invariance argument.
r,\
( (d) F,ll of the above.
3. (B)
An improper
prior
(a) is too subjective
for scientific
investigations.
(b) expresses
ignorance
about the underlying parameter
values.
(q) is integrable
over the parameter spape.
.t-\ " ,
/ tal Ir not necessarily
any of the
above.
\-/
4. (8) Flequentist
inferencqris
basgd on mea;ures of initial precision,
while Bayesian
inference
is baseci on measure" o; {rha/ PteetSt'o0
5. (8) Linflgy's paradox occurs when a frequentist P-value for a nullThypothesis
,F/6 is
StllA 4 and a Bayesian posterior probability for I/e is /49e
6. (B) The mean of tbe posterior distribution is often a weighted average
of what two
quantities?
fl. p//o/ ,tteorl on/ a cofltilorl fr"nrur/:/
es/|hol", /
I
pf2

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STAT 632-

October 29, 2008 Midterm Exam

Instructions: Write all your answerson the test paper provided. Circle what you think is the correct answer on multiple choice questions, and be spectficon short-answer quest'ions. Point values are indicated in parentheses.Good luck!

  1. (8) Let )/ be the set of all possible^ observations. A class P of priors is conjugate for a qiven set of likelihoods .F if

( ( u ) J " (. l d^ e P f o r a l l n € P a n d a l l a e y.

(b) "( .lA) e P for some n e P and all A e y.

(c) r e P impliesthat n('lg) is aproper distributionfor eachA ey.

(d) the likelihood is ploportional to some member of P for each g ey.

  1. (8) The Jeffreys noninformative prior is (a) sometimesimproper. (^).

(b) proportional to the squa,reroot of the determinant of the information matrix. (pI derived from an invariance argument.

r , \

( (d) F,ll of the above.

  1. (B) An improper prior

(a) is too subjective for scientific investigations. (b) expressesignorance about the underlying parameter values.

(q) is integrable over the parameter spape.

.t-\

/ tal Ir not necessarilyanyof the^ above.

-/
  1. (8) Flequentist inferencqrisbasgdon mea;uresof initial precision,while Bayesianinference is basecion measure"o; {rha/ (^) PteetSt'o
  2. (8) Linflgy's paradox occurs when a frequentist P-value for a nullThypothesis ,F/6is StllA (^4) and a Bayesian posterior probability for I/e is (^) /49e
  3. (B) The mean of tbe posterior distribution is often a weighted average of what two quantities? fl. p//o/ ,tteorl^ on/ a^ cofltilorl^ fr"nrur/:/

es/|hol",

/

I

  1. (6) Is the following statement true or false? "Computation of the posterior requires the entire data set and not just a sufficient statistic." l- I

fa/Oe.

  1. (18) Define the terms Bayesi,ancred,i,bleregion and hi.ghestposterior densi,tyregi.on.

Suu-

/o- 7/ of il. c/ss 4't'.

9. (12)^ The posteriorodds ratio, asf ar, is usedfor what purpose?

[ /,"/

ona 177"fi*n vs. a/,of/e/.

  1. (16) The posterior distribution may be approximated by a multivariate normal under appropriate regularity conditions. One version of this result dependson the prior distribu- tion. Give an explicit statement of this version of the result. Note: You do not have to state the regularity conditions,

n c.. - t { ^ t r J \ - V , / / ^ 1 , - 1 - / / I

(/cftrretrKn//o/o)=Xt"/ofe)X(") aty'/t

6 J. fi" povrtr,urto/r.^ [f'H(;) /s fr.

H"sslat "i^4 flrt^ il.^ no//t/e/o///ox, /

I /( I tt/-\

\

/V u3t frle)