probability of concordance and discordance
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Define the probability of concordance $(pi_c)$ and probability of discordance $(pi_d)$. Obtain an unbiased estimate of $tau = pi_c-pi_d$
I know what is concordance and discordance (usually use it to find Kendall's tau). Knowing that, I guessed that probability of concordance is $P(X_1>X_2, Y_1>Y_2)$ or something like this. But I don't really rely on guess. What I want is a article/book where it is documented or if someone gives answer of this specific question here (that will be very helpful), so that I ca answer the question clearly. Anyway, thanks for any help.
probability statistics reference-request statistical-inference
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up vote
3
down vote
favorite
Define the probability of concordance $(pi_c)$ and probability of discordance $(pi_d)$. Obtain an unbiased estimate of $tau = pi_c-pi_d$
I know what is concordance and discordance (usually use it to find Kendall's tau). Knowing that, I guessed that probability of concordance is $P(X_1>X_2, Y_1>Y_2)$ or something like this. But I don't really rely on guess. What I want is a article/book where it is documented or if someone gives answer of this specific question here (that will be very helpful), so that I ca answer the question clearly. Anyway, thanks for any help.
probability statistics reference-request statistical-inference
add a comment |Â
up vote
3
down vote
favorite
up vote
3
down vote
favorite
Define the probability of concordance $(pi_c)$ and probability of discordance $(pi_d)$. Obtain an unbiased estimate of $tau = pi_c-pi_d$
I know what is concordance and discordance (usually use it to find Kendall's tau). Knowing that, I guessed that probability of concordance is $P(X_1>X_2, Y_1>Y_2)$ or something like this. But I don't really rely on guess. What I want is a article/book where it is documented or if someone gives answer of this specific question here (that will be very helpful), so that I ca answer the question clearly. Anyway, thanks for any help.
probability statistics reference-request statistical-inference
Define the probability of concordance $(pi_c)$ and probability of discordance $(pi_d)$. Obtain an unbiased estimate of $tau = pi_c-pi_d$
I know what is concordance and discordance (usually use it to find Kendall's tau). Knowing that, I guessed that probability of concordance is $P(X_1>X_2, Y_1>Y_2)$ or something like this. But I don't really rely on guess. What I want is a article/book where it is documented or if someone gives answer of this specific question here (that will be very helpful), so that I ca answer the question clearly. Anyway, thanks for any help.
probability statistics reference-request statistical-inference
probability statistics reference-request statistical-inference
edited Aug 31 at 9:59
joriki
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167k10180333
asked Aug 31 at 7:41
Stat_prob_001
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283112
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To quote from this textbook Chapter 5.2.3.1
More specifically, a pair of observations is concordant if the
observation with the larger value of $X_1$ also has the larger value
for $X_2$. If $(X_1,X_2)$ and $(X_1',X_2')$ are independent and
identically distributed then they are said to be concordant if $ (X_1- X_1')(X_2-X_2')>0$
, whereas they are said to be discordant when the reverse inequality is valid. Henceforth we denote
$$textPr[textconcordance]=textPr[(X_1 - X_1')(X_2-X_2')>0] $$
and $$ textPr[textdiscordance]=textPr[(X_1 -X_1')(X_2-X_2')<0]. $$
In Chapter 5.2.7.1 they also discuss the issue of ties, if that is a concern for you.
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
To quote from this textbook Chapter 5.2.3.1
More specifically, a pair of observations is concordant if the
observation with the larger value of $X_1$ also has the larger value
for $X_2$. If $(X_1,X_2)$ and $(X_1',X_2')$ are independent and
identically distributed then they are said to be concordant if $ (X_1- X_1')(X_2-X_2')>0$
, whereas they are said to be discordant when the reverse inequality is valid. Henceforth we denote
$$textPr[textconcordance]=textPr[(X_1 - X_1')(X_2-X_2')>0] $$
and $$ textPr[textdiscordance]=textPr[(X_1 -X_1')(X_2-X_2')<0]. $$
In Chapter 5.2.7.1 they also discuss the issue of ties, if that is a concern for you.
add a comment |Â
up vote
2
down vote
accepted
To quote from this textbook Chapter 5.2.3.1
More specifically, a pair of observations is concordant if the
observation with the larger value of $X_1$ also has the larger value
for $X_2$. If $(X_1,X_2)$ and $(X_1',X_2')$ are independent and
identically distributed then they are said to be concordant if $ (X_1- X_1')(X_2-X_2')>0$
, whereas they are said to be discordant when the reverse inequality is valid. Henceforth we denote
$$textPr[textconcordance]=textPr[(X_1 - X_1')(X_2-X_2')>0] $$
and $$ textPr[textdiscordance]=textPr[(X_1 -X_1')(X_2-X_2')<0]. $$
In Chapter 5.2.7.1 they also discuss the issue of ties, if that is a concern for you.
add a comment |Â
up vote
2
down vote
accepted
up vote
2
down vote
accepted
To quote from this textbook Chapter 5.2.3.1
More specifically, a pair of observations is concordant if the
observation with the larger value of $X_1$ also has the larger value
for $X_2$. If $(X_1,X_2)$ and $(X_1',X_2')$ are independent and
identically distributed then they are said to be concordant if $ (X_1- X_1')(X_2-X_2')>0$
, whereas they are said to be discordant when the reverse inequality is valid. Henceforth we denote
$$textPr[textconcordance]=textPr[(X_1 - X_1')(X_2-X_2')>0] $$
and $$ textPr[textdiscordance]=textPr[(X_1 -X_1')(X_2-X_2')<0]. $$
In Chapter 5.2.7.1 they also discuss the issue of ties, if that is a concern for you.
To quote from this textbook Chapter 5.2.3.1
More specifically, a pair of observations is concordant if the
observation with the larger value of $X_1$ also has the larger value
for $X_2$. If $(X_1,X_2)$ and $(X_1',X_2')$ are independent and
identically distributed then they are said to be concordant if $ (X_1- X_1')(X_2-X_2')>0$
, whereas they are said to be discordant when the reverse inequality is valid. Henceforth we denote
$$textPr[textconcordance]=textPr[(X_1 - X_1')(X_2-X_2')>0] $$
and $$ textPr[textdiscordance]=textPr[(X_1 -X_1')(X_2-X_2')<0]. $$
In Chapter 5.2.7.1 they also discuss the issue of ties, if that is a concern for you.
edited Sep 10 at 17:58
answered Sep 7 at 18:52
g g
1,004415
1,004415
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