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.










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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.










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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.










      share|cite|improve this question
















      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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      edited Aug 31 at 9:59









      joriki

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      asked Aug 31 at 7:41









      Stat_prob_001

      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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            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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              up vote
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              down vote



              accepted
              +50










              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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                up vote
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                accepted
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                up vote
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                down vote



                accepted
                +50




                +50




                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.






                share|cite|improve this answer














                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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                edited Sep 10 at 17:58

























                answered Sep 7 at 18:52









                g g

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