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IMST 2008 / FIM XVI Decision theoretic Bayesian hypothesis testing with focus on skewed alternatives By Naveen K. Bansal Ru Sheng Marquette University Milwaukee, WI 53051 Email: [email protected] http://www.mscs.mu.edu/~naveen/

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Decision theoretic Bayesian hypothesis testing with focus on skewed alternatives. By Naveen K. Bansal Ru Sheng Marquette University Milwaukee, WI 53051 Email: [email protected] http://www.mscs.mu.edu/~naveen/. IMST 2008 / FIM XVI. Directional Error (Type III error): - PowerPoint PPT Presentation

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Page 1: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

Decision theoretic Bayesian hypothesis testing with focus on skewed alternatives

By

Naveen K. BansalRu Sheng

Marquette UniversityMilwaukee, WI 53051

Email: [email protected]

http://www.mscs.mu.edu/~naveen/

Page 2: IMST 2008 / FIM XVI

0:1,0:1)0,(0:0

),P( :Modely Probabilit

HHvssayH

IMST 2008 / FIM XVI

Directional Error (Type III error):

Sarkar and Zhou (2008, JSPI)Shaffer (2002, Psychological Methods)Finner ( 1999, AS)Lehmann (1952, AMS; 1957, AMS)

Main points of these work is that if the objective is to find the true Direction of the alternative after rejecting the null, then a Type III error must be also controlled.

Type III error is defined as P( false directional error if the null is rejected). The traditional method does not control the directional error. For example,

0. if occurserror an , and ,|| 2/2/ tttt

Page 3: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

.action gfor takin loss),(},1,0,1{

)(0,in containedsupport ith density w)(1

,0)(-in containedsupport ith density w)(1

)0()(1),0()(0),0()(1)(1-

where

)(11)(00)(11)(

0:1,0:1,)0,(0:0

),P( :Modely Probabilit

AaaLA

g

g

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ppp

HHsayH

Page 4: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

same. theare error) I (Type ),0( for which rules all of class theconsider

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space thecomparingby compared becan rulesdecision ,prior fixed aFor

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Page 5: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

direction. positive in therisk averagehan smaller t be willdirection negative in the

rule Bayes theofrisk average e then th),11( 1an likely th more is 1t known tha isit apriori if that implies theoremThis:

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Theorem

Page 6: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

Relationship with the False Discovery Rates

s.discoverie false theseof average weighted theis )( rate.dicovery -non false expected as )(

and rate,discovery positive-non false expected as dinterprete becan )(Similarly framwork.Bayesian in the ratediscovery negative-non false expected iswhich

)]1|0([1

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Page 7: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

rule Bayes randomized-non}1,0,1{

tindependen ,...,2,1),(11)(00)(11~

0:1,0:1,0:0

,...,2,1),,;(~i

withdatat independen },...,2,1,{

iB

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ProblemHupotheses Multiple

Page 8: IMST 2008 / FIM XVI

An Application:

An experiment was conducted to see the effect of a sequence knockdown from non-coding genes. Hypothesis was that this knockdown will cause the overexpression of the mRNAs of the coding genes. The implication of this is that this will shows how the non-coding genes interact with coding genes. In other words, non-coding genes also play a part in protein synthesis.

Here it can be assumed that that the effect of knockdown on the coding genes (if there is any) would be mostly overexpression of mRNAs than underexpression of mRNAs. The objective would be to detect as many of overexpressed genes as possible.

IMST 2008 / FIM XVI

Page 9: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

Accept Accept Accept Total

is true

is true

is true

Total

U 1V 2V 0m

1H1T 1S 2W 1m

1H2T 1W 2S 2m

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Table 1Possible outcomes from hypothesis tests

m

m

)2003,(]0|[]0|[

][][

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RWVEFDR

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Page 10: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

)1|1()1|0()(1

)1|1()1|0()(1

then trials,l trinomiaidenticalandt independen are ,...,2,1),1,0,1( If ly.respective 1

and 0,1 wrt of onsdistributi marginal theare 1,0,1

where,1)1(0)0(1)1(~| that Assume

.,...,2,1each for ly,respective ,1 and 1 accepting of

regions thebe 1 and 1Let .,...,2,1 statisticson test based are testshypothesis that Suppose :

THPTHPpFDR

and

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miiHiHiHiTFFF

FiHIFiHIFiHIiidiHiT

miiHiHmTTTm

2 Theorem

Page 11: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

This theorem and our previous discussion on the Bayes decision rule implies that under the independent hypotheses testing setting, the Bayes rule obtained from a single test problem

which minimizes the average expected false discoveries, can be used under multiple hypotheses problem. The false discovery rates of this procedure can be obtained by computing the posterior probabilities.

,0:1,0:1 vs.0:0 HHH

2 and 1 pFDRpFDR

)1|1()1|0()(1

)1|1()1|0()(1

BHPBHPBpFDR

BHPBHPBpFDR

The prior probabilities will determine whether the positive hypothesis or the negative hypothesiswill be more frequently correctly detected.

1 and 1 pp1H 1H

Page 12: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

Computation of Bayes Rule:

].,1|)1,([ and ],1|)1,([ computing and)|1( and ),|0(),|1( computing invole partsn Computatio

ly.respective )(1 and )(1under of densities posterior theare ),1|( and ),1|( and ly;respective 1 and ,0,1 ofiesprobabilitposterior theare )|1( and ),|0(),|1( Here

),1|()|1()0()|0(),1|()|1()|(

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min]|),([:)1,0,1({)(

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Page 13: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

))],(/)0,([log()(

is )( of choice good One null. thefrom departure of magnitude hereflect t )( Here,

.constants negative-non some are 1 and 1,0 where

))(1(1)(1)),(1(1)(1 )),((10)(0

be wouldchoice goodA

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00)1,(,0)(1

00)1,(,0)(0

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qqq

qLqLqL

Choice of the loss function:

Page 14: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

)1|(1)()1|(1

if )1(1select Otherwise,

.0

1)1|(

)0|( and

0

1)1|(

)0|(

if 0reject ds,other worIn

.under density marginal theis )|( Here

)}1,0,1),|(max()|(:{

bygiven is rule Bayes theloss, 1"-"0 he Under t:

loss. 1"-"0 a have then we,1)(1)(1)( If

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p

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XX

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X

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X

XX

XX

3 Theorem

Page 15: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

ondistributi )1,0( half-right:1

ondistributi )1,0( half-left:1

)(11)0(0)(11)(:Prior

0:1,0:1.0:0

)1,( :Modely Probabilit

:Example

N

N

pIpp

HHvsH

N

Page 16: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

)1/0(11 and )1/0(1

1

toequivalent isregion rejection theThus ly.respective functions,increasing and decreasinglly monotonica are 1 and 1 that Note

)1

())1(2

22exp(

12)(1

)1

())1(2

22exp(

12)(1

where),(11)()(11 if )1(1select otherwise

,1/0)(1 and 1/0)(1

if 0reject rule Bayes theloss, 1"-"0 he Under t:

ppTxppTx

TT

nnx

nxn

nxT

andnnx

nxn

nxT

xTpxTpHH

ppxTppxT

H

4 Theorem

Page 17: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

Power Comparison:

line dotted red : testunbiased UMPline) blue :22.01,39.00,39.01 test withBayes

unbiased. UMPisregion rejection the,11When

ppp

pp

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

mean value

prob

of r

ejec

tion

Page 18: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

mean value

prob

of r

ejec

tion

line dotted red : testunbiased UMPline) blue :39.01,39.00,22.01 test withBayes ppp

Page 19: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

where),(1)|1()()(1)|1E( if )1(1select otherwise

,)|1(

)|0()(1 and

)|1(

)|0()(1

if 0reject rule Bayes theloss, 1"-"0 he Under t:5

result. following get the weproblem, normal For the

ly.respective )|1( and ),|0(),|1(by 1,0,1 replace tobe woulddifference

only e that thsee easy to isIt prior.Dirichlet aconsider can One

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pExT

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XX

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Theorem

unknown? are 1pand,0p,1p ifWhat

Page 20: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

Another Approach:

values.in dispersion the and values,in skewness thereflects Here

).0()()(2)('

normal-skew example,for prior; skewed a is )(' where

),(')01()0(0p)(

prior heConsider t

I

pI

Page 21: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

mean value

prob

of r

ejec

tion

Power Comparison of a skew-normal Bayes with the UMP test

Page 22: IMST 2008 / FIM XVI

IMST 2008 / FIM XVI