{"history":[],"article":{"id":"7200abb8-d80d-4347-a012-bbb64e4a288c","title":"Kendall rank correlation coefficient","slug":"kendall-rank-correlation-coefficient","content":":::summary\n:::\n\n# \nIn [statistics](https://bliptext.com/articles/[statistic](https://bliptext.com/articles/statistic)s), the **Kendall rank correlation coefficient**, commonly referred to as **Kendall's [τ](https://bliptext.com/articles/) coefficient** (after the Greek letter [τ](https://bliptext.com/articles/), tau), is a [statistic](https://bliptext.com/articles/statistic) used to measure the [ordinal association](https://bliptext.com/articles/ordinal-association) between two measured quantities.\nA **τ test** is a [non-parametric](https://bliptext.com/articles/non-parametric-statistics) [hypothesis test](https://bliptext.com/articles/hypothesis-test) for statistical dependence based on the τ coefficient.\nIt is a measure of [rank correlation](https://bliptext.com/articles/rank-correlation): the similarity of the orderings of the data when [ranked](https://bliptext.com/articles/ranked) by each of the quantities.\nIt is named after [Maurice Kendall](https://bliptext.com/articles/maurice-kendall), who developed it in 1938, though [Gustav Fechner](https://bliptext.com/articles/gustav-fechner) had proposed a similar measure in the context of [time series](https://bliptext.com/articles/time-series) in 1897.\nIntuitively, the Kendall correlation between two variables will be high when observations have a similar (or identical for a correlation of 1) [rank](https://bliptext.com/articles/ranking) (i.e. relative position label of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables, and low when observations have a dissimilar (or fully different for a correlation of −1) [rank](https://bliptext.com/articles/ranking) between the two variables.\nBoth Kendall's \\tau and [Spearman's \\rho](https://bliptext.com/articles/spearman-s-rank-correlation-coefficient) can be formulated as special cases of a more [general correlation coefficient](https://bliptext.com/articles/general-correlation-coefficient).\nIts notions of [concordance](https://bliptext.com/articles/concordant-pair) and discordance also appear in other areas of statistics, like the [Rand index](https://bliptext.com/articles/rand-index) in [cluster analysis](https://bliptext.com/articles/cluster-analysis).\n\n# Definition\nLet be a set of observations of the joint random variables X and Y, such that all the values of (x_i) and (y_i) are unique.\n[](https://bliptext.com/articles/)([](https://bliptext.com/articles/)S[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)h[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) 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[](https://bliptext.com/articles/)o[](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)h[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/)r[](https://bliptext.com/articles/)w[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)s[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)h[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/)y[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)r[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)s[](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)d[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)o[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)b[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)d[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)s[](https://bliptext.com/articles/)c[](https://bliptext.com/articles/)o[](https://bliptext.com/articles/)r[](https://bliptext.com/articles/)d[](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)n[](https://bliptext.com/articles/)t[](https://bliptext.com/articles/).[](https://bliptext.com/articles/)\nIn the absence of ties, the Kendall τ coefficient is defined as:\nfor i < j < n where is the [binomial coefficient](https://bliptext.com/articles/binomial-coefficient) for the number of ways to choose two items from n items.\nThe number of discordant pairs is equal to the [inversion number](https://bliptext.com/articles/inversion-discrete-mathematics) that permutes the y-sequence into the same order as the x-sequence.\n# [All points in the gray area are concordant and all points in the white area are discordant with respect to point (X_1, Y_1). With n=30 points, there are a total of possible point pairs. In this example there are 395 concordant point pairs and 40 discordant point pairs, leading to a Kendall rank correlation coefficient of 0.816. | upload.wikimedia.org/wikipedia/commons/0/0e/Concordant///Points///Kendall///Correlation.svg]\n\n## Properties\nThe [denominator](https://bliptext.com/articles/denominator) is the total number of pair combinations, so the coefficient must be in the range −1 ≤ τ ≤ 1.\n\n# Hypothesis test\nThe Kendall rank coefficient is often used as a [test statistic](https://bliptext.com/articles/test-statistic) in a [statistical hypothesis test](https://bliptext.com/articles/statistical-hypothesis-testing) to establish whether two variables may be regarded as statistically dependent.\nThis test is [non-parametric](https://bliptext.com/articles/non-parametric-statistics), as it does not rely on any assumptions on the distributions of X or Y or the distribution of (X,Y).\nUnder the [null hypothesis](https://bliptext.com/articles/null-hypothesis) of independence of X and Y, the [sampling distribution](https://bliptext.com/articles/sampling-distribution) of τ has an [expected value](https://bliptext.com/articles/expected-value) of zero.\nThe precise distribution cannot be characterized in terms of common distributions, but may be calculated exactly for small samples; for larger samples, it is common to use an approximation to the [normal distribution](https://bliptext.com/articles/normal-distribution), with mean zero and variance.\n**Theorem.** If the samples are independent, then the variance of \\tau_A is given by.\n\n# Case of standard normal distributions\nIf are IID samples from the same jointly normal distribution with a known [Pearson correlation coefficient](https://bliptext.com/articles/pearson-correlation-coefficient) r, then the expectation of Kendall rank correlation has a closed-form formula.\nThe name is credited to Richard Greiner (1909) by [P. A. P. Moran](https://bliptext.com/articles/p-a-p-moran).\n\n# Accounting for ties\nA pair is said to be tied if and only if or ; a tied pair is neither concordant nor discordant.\nWhen tied pairs arise in the data, the coefficient may be modified in a number of ways to keep it in the range [−1, 1]:\n\n## Tau-a\nThe Tau statistic defined by Kendall in 1938 was retrospectively renamed Tau-a.\nIt represents the strength of positive or negative association of two quantitative or [ordinal](https://bliptext.com/articles/ordinal-level) variables without any adjustment for ties.\nIt is defined as:\nwhere nc, nd and n0 are defined as in the next section.\nWhen ties are present, and, the coefficient can never be equal to +1 or -1.\nEven a perfect equality of the two variables (X=Y) leads to a Tau-a < 1.\n\n## Tau-b\nThe Tau-b statistic, unlike Tau-a, makes adjustments for ties.\nThis Tau-b was first decribed by Kendall in 1945 under the name Tau-w as an extension of the original Tau statistic supporting ties.\nValues of Tau-b range from −1 (100% negative association, or perfect disagreement) to +1 (100% positive association, or perfect agreement).\nIn case of the absence of association, Tau-b is equal to zero.\nThe Kendall Tau-b coefficient is defined as :\nwhere\nA simple algorithm developed in BASIC computes Tau-b coefficient using an alternative formula.\nBe aware that some statistical packages, e.g. SPSS, use alternative formulas for computational efficiency, with double the 'usual' number of concordant and discordant pairs.\n\n## Tau-c\nTau-c (also called Stuart-Kendall Tau-c) was first defined by Stuart in 1953.\nContrary to Tau-b, Tau-c can be equal to +1 or -1 for non-square (i.e. rectangular) [contingency tables](https://bliptext.com/articles/contingency-tables), i.e. when the underlying scale of both variables have different number of possible values.\nFor instance, if the variable X has a continuous uniform distribution between 0 and 100 and Y is a dichotomous variable equal to 1 if X ≥ 50 and 0 if X < 50, the Tau-c statistic of X and Y is equal to 1 while Tau-b is equal to 0.707.\nA Tau-C equal to 1 can be interpreted as the best possible positive correlation conditional to marginal distributions while a Tau-B equal to 1 can be interpreted as the perfect positive monotonic correlation where the distribution of X conditional to Y has zero variance and the distribution of Y conditional to X has zero variance so that a bijective function f with f(X)=Y exists.\nThe Stuart-Kendall Tau-c coefficient is defined as:\nwhere\n\n# Significance tests\nWhen two quantities are statistically dependent, the distribution of \\tau is not easily characterizable in terms of known distributions.\nHowever, for \\tau_A the following statistic, z_A, is approximately distributed as a standard normal when the variables are statistically independent:\nwhere.\nThus, to test whether two variables are statistically dependent, one computes z_A, and finds the cumulative probability for a standard normal distribution at -|z_A|.\nFor a 2-tailed test, multiply that number by two to obtain the p-value.\nIf the p-value is below a given significance level, one rejects the null hypothesis (at that significance level) that the quantities are statistically independent.\nNumerous adjustments should be added to z_A when accounting for ties.\nThe following statistic, z_B, has the same distribution as the \\tau_B distribution, and is again approximately equal to a standard normal distribution when the quantities are statistically independent:\nwhere\nThis is sometimes referred to as the Mann-Kendall test.\n\n# Algorithms\nThe direct computation of the numerator n_c - n_d, involves two nested iterations, as characterized by the following pseudocode:\nnumer := 0\n**for** i := 2..N **do**\n**for** j := 1..(i − 1) **do**\nnumer := numer + sign(x[i] − x[j]) × sign(y[i] − y[j])\n**return** numer\nAlthough quick to implement, this algorithm is O(n^2) in complexity and becomes very slow on large samples.\nA more sophisticated algorithm built upon the [Merge Sort](https://bliptext.com/articles/merge-sort) algorithm can be used to compute the numerator in time.\nBegin by ordering your data points sorting by the first quantity, x, and secondarily (among ties in x) by the second quantity, y. With this initial ordering, y is not sorted, and the core of the algorithm consists of computing how many steps a [Bubble Sort](https://bliptext.com/articles/bubble-sort) would take to sort this initial y. An enhanced [Merge Sort](https://bliptext.com/articles/merge-sort) algorithm, with O(n \\log n) complexity, can be applied to compute the number of swaps, S(y), that would be required by a [Bubble Sort](https://bliptext.com/articles/bubble-sort) to sort y_i.\nThen the numerator for \\tau is computed as:\nwhere n_3 is computed like n_1 and n_2, but with respect to the joint ties in x and y.\nA [Merge Sort](https://bliptext.com/articles/merge-sort) partitions the data to be sorted, y into two roughly equal halves, and, then sorts each half recursive, and then merges the two sorted halves into a fully sorted vector.\nThe number of [Bubble Sort](https://bliptext.com/articles/bubble-sort) swaps is equal to:\nwhere and are the sorted versions of and, and characterizes the [Bubble Sort](https://bliptext.com/articles/bubble-sort) swap-equivalent for a merge operation.\nis computed as depicted in the following pseudo-code:\n**function** M(L[1..n], R[1..m]) **is**\ni := 1\nj := 1\nnSwaps := 0\n**while** i ≤ n **and** j ≤ m **do**\n**if** R[j] < L[i] **then**\nnSwaps := nSwaps + n − i + 1\nj := j + 1\n**else**\ni := i + 1\n**return** nSwaps\nA side effect of the above steps is that you end up with both a sorted version of x and a sorted version of y. With these, the factors t_i and u_j used to compute \\tau_B are easily obtained in a single linear-time pass through the sorted arrays.\n\n# Approximating Kendall rank correlation from a stream\nEfficient algorithms for calculating the Kendall rank correlation coefficient as per the standard estimator have time complexity.\nHowever, these algorithms necessitate the availability of all data to determine observation ranks, posing a challenge in sequential data settings where observations are revealed incrementally.\nFortunately, algorithms do exist to estimate approximations of the Kendall rank correlation coefficient in sequential settings.\nThese algorithms have O(1) update time and space complexity, scaling efficiently with the number of observations.\nConsequently, when processing a batch of n observations, the time complexity becomes O(n), while space complexity remains a constant O(1).\nThe first such algorithm presents an approximation to the Kendall rank correlation coefficient based on coarsening the joint distribution of the random variables.\nNon-stationary data is treated via a moving window approach.\nThis algorithm is simple and is able to handle discrete random variables along with continuous random variables without modification.\nThe second algorithm is based on Hermite series estimators and utilizes an alternative estimator for the exact Kendall rank correlation coefficient i.e. for the probability of concordance minus the probability of discordance of pairs of bivariate observations.\nThis alternative estimator also serves as an approximation to the standard estimator.\nThis algorithm is only applicable to continuous random variables, but it has demonstrated superior accuracy and potential speed gains compared to the first algorithm described, along with the capability to handle non-stationary data without relying on sliding windows.\nAn efficient implementation of the Hermite series based approach is contained in the R package package [hermiter](https://bliptext.com/articles/undefined).\n\n# Software Implementations","current_revision":null,"created_by":"kACnuQmH5k9Nr5VNVEpVeg7XpvUmMctD","created_at":"2025-01-26T08:32:39.687Z","updated_at":"2025-01-26T08:32:39.687Z","search_vector":"'coeffici':4 'correl':3 'kendal':1 'rank':2","search_content":" \n \n\n# \nIn [[statistic](https://bliptext.com/articles/statistic)s](https://bliptext.com/articles/[statistic](https://bliptext.com/articles/statistic)s), the **Kendall rank correlation coefficient**, commonly referred to as **Kendall's [τ](https://bliptext.com/articles/) coefficient** (after the Greek letter [τ](https://bliptext.com/articles/), tau), is a [statistic](https://bliptext.com/articles/statistic) used to measure the [ordinal association](https://bliptext.com/articles/ordinal-association) between two measured quantities.\nA **τ test** is a [non-parametric](https://bliptext.com/articles/non-parametric-statistics) [hypothesis test](https://bliptext.com/articles/hypothesis-test) for statistical dependence based on the τ coefficient.\nIt is a measure of [rank correlation](https://bliptext.com/articles/rank-correlation): the similarity of the orderings of the data when [ranked](https://bliptext.com/articles/ranked) by each of the quantities.\nIt is named after [Maurice Kendall](https://bliptext.com/articles/maurice-kendall), who developed it in 1938, though [Gustav Fechner](https://bliptext.com/articles/gustav-fechner) had proposed a similar measure in the context of [time series](https://bliptext.com/articles/time-series) in 1897.\nIntuitively, the Kendall correlation between two variables will be high when observations have a similar (or identical for a correlation of 1) [rank](https://bliptext.com/articles/ranking) (i.e. relative position label of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables, and low when observations have a dissimilar (or fully different for a correlation of −1) [rank](https://bliptext.com/articles/ranking) between the two variables.\nBoth Kendall's \\tau and [Spearman's \\rho](https://bliptext.com/articles/spearman-s-rank-correlation-coefficient) can be formulated as special cases of a more [general correlation coefficient](https://bliptext.com/articles/general-correlation-coefficient).\nIts notions of [concordance](https://bliptext.com/articles/concordant-pair) and discordance also appear in other areas of statistics, like the [Rand index](https://bliptext.com/articles/rand-index) in [cluster analysis](https://bliptext.com/articles/cluster-analysis).\n\n# Definition\nLet be a set of observations of the joint random variables X and Y, such that all the values of (x_i) and (y_i) are unique.\n[](https://bliptext.com/articles/)([](https://bliptext.com/articles/)S[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) 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rticles/)s[](https://bliptext.com/articles/).[](https://bliptext.com/articles/))[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)A[](https://bliptext.com/articles/)n[](https://bliptext.com/articles/)y[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)p[](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)r[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)o[](https://bliptext.com/articles/)f[](https://bliptext.com/articles/) [](https://bliptext.com/articles/) [](https://bliptext.com/articles/)o[](https://bliptext.com/articles/)b[](https://bliptext.com/articles/)s[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/)r[](https://bliptext.com/a rticles/)v[](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)o[](https://bliptext.com/articles/)n[](https://blip text.com/articles/)s[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)([](https://bliptext.com/articles/)x[](https://bliptext.com/articles/)_[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/),[](https://bliptext.com/a rticles/)y[](https://bliptext.com/articles/)_[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/))[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)n[](https://bliptext.com/articles/)d[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)([](https://bliptext.com/articles/)x[](https://bliptext.com/articles/)_[](https://bliptext.com/articles/)j[](https://bliptext.com/articles/),[](https://bliptext.com/a rticles/)y[](https://bliptext.com/articles/)_[](https://bliptext.com/articles/)j[](https://bliptext.com/articles/))[](https://bliptext.com/articles/),[](https://bliptext.com/articles/) 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[](https://bliptext.com/articles/)y[](https://bliptext.com/articles/)_[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)<[](https://bliptext.com/articles/)y[](https://bliptext.com/a rticles/)_[](https://bliptext.com/articles/)j[](https://bliptext.com/articles/);[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)o[](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)h[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/)r[](https://bliptext.com/a rticles/)w[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)s[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)h[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/)y[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)r[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)s[](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)d[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)t[](https://bliptext.com/articles/)o[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)b[](https://bliptext.com/articles/)e[](https://bliptext.com/articles/) [](https://bliptext.com/articles/)d[](https://bliptext.com/articles/)i[](https://bliptext.com/articles/)s[](https://bliptext.com/articles/)c[](https://bliptext.com/articles/)o[](https://bliptext.com/a rticles/)r[](https://bliptext.com/articles/)d[](https://bliptext.com/articles/)a[](https://bliptext.com/articles/)n[](https://bliptext.com/articles/)t[](https://bliptext.com/articles/).[](https://blip text.com/articles/)\nIn the absence of ties, the Kendall τ coefficient is defined as:\nfor i < j < n where is the [binomial coefficient](https://bliptext.com/articles/binomial-coefficient) for the number of ways to choose two items from n items.\nThe number of discordant pairs is equal to the [inversion number](https://bliptext.com/articles/inversion-discrete-mathematics) that permutes the y-sequence into the same order as the x-sequence.\n# [All points in the gray area are concordant and all points in the white area are discordant with respect to point (X_1, Y_1). With n=30 points, there are a total of possible point pairs. In this example there are 395 concordant point pairs and 40 discordant point pairs, leading to a Kendall rank correlation coefficient of 0.816. | upload.wikimedia.org/wikipedia/commons/0/0e/Concordant///Points///Kendall///Correlation.svg]\n\n## Properties\nThe [denominator](https://bliptext.com/articles/denominator) is the total number of pair combinations, so the coefficient must be in the range −1 ≤ τ ≤ 1.\n\n# Hypothesis test\nThe Kendall rank coefficient is often used as a [test statistic](https://bliptext.com/articles/test-statistic) in a [statistical hypothesis test](https://bliptext.com/articles/statistical-hypothesis-testing) to establish whether two variables may be regarded as statistically dependent.\nThis test is [non-parametric](https://bliptext.com/articles/non-parametric-statistics), as it does not rely on any assumptions on the distributions of X or Y or the distribution of (X,Y).\nUnder the [null hypothesis](https://bliptext.com/articles/null-hypothesis) of independence of X and Y, the [sampling distribution](https://bliptext.com/articles/sampling-distribution) of τ has an [expected value](https://bliptext.com/articles/expected-value) of zero.\nThe precise distribution cannot be characterized in terms of common distributions, but may be calculated exactly for small samples; for larger samples, it is common to use an approximation to the [normal distribution](https://bliptext.com/articles/normal-distribution), with mean zero and variance.\n**Theorem.** If the samples are independent, then the variance of \\tau_A is given by.\n\n# Case of standard normal distributions\nIf are IID samples from the same jointly normal distribution with a known [Pearson correlation coefficient](https://bliptext.com/articles/pearson-correlation-coefficient) r, then the expectation of Kendall rank correlation has a closed-form formula.\nThe name is credited to Richard Greiner (1909) by [P. A. P. Moran](https://bliptext.com/articles/p-a-p-moran).\n\n# Accounting for ties\nA pair is said to be tied if and only if or ; a tied pair is neither concordant nor discordant.\nWhen tied pairs arise in the data, the coefficient may be modified in a number of ways to keep it in the range [−1, 1]:\n\n## Tau-a\nThe Tau statistic defined by Kendall in 1938 was retrospectively renamed Tau-a.\nIt represents the strength of positive or negative association of two quantitative or [ordinal](https://bliptext.com/articles/ordinal-level) variables without any adjustment for ties.\nIt is defined as:\nwhere nc, nd and n0 are defined as in the next section.\nWhen ties are present, and, the coefficient can never be equal to +1 or -1.\nEven a perfect equality of the two variables (X=Y) leads to a Tau-a < 1.\n\n## Tau-b\nThe Tau-b statistic, unlike Tau-a, makes adjustments for ties.\nThis Tau-b was first decribed by Kendall in 1945 under the name Tau-w as an extension of the original Tau statistic supporting ties.\nValues of Tau-b range from −1 (100% negative association, or perfect disagreement) to +1 (100% positive association, or perfect agreement).\nIn case of the absence of association, Tau-b is equal to zero.\nThe Kendall Tau-b coefficient is defined as :\nwhere\nA simple algorithm developed in BASIC computes Tau-b coefficient using an alternative formula.\nBe aware that some statistical packages, e.g. SPSS, use alternative formulas for computational efficiency, with double the 'usual' number of concordant and discordant pairs.\n\n## Tau-c\nTau-c (also called Stuart-Kendall Tau-c) was first defined by Stuart in 1953.\nContrary to Tau-b, Tau-c can be equal to +1 or -1 for non-square (i.e. rectangular) [contingency tables](https://bliptext.com/articles/contingency-tables), i.e. when the underlying scale of both variables have different number of possible values.\nFor instance, if the variable X has a continuous uniform distribution between 0 and 100 and Y is a dichotomous variable equal to 1 if X ≥ 50 and 0 if X < 50, the Tau-c statistic of X and Y is equal to 1 while Tau-b is equal to 0.707.\nA Tau-C equal to 1 can be interpreted as the best possible positive correlation conditional to marginal distributions while a Tau-B equal to 1 can be interpreted as the perfect positive monotonic correlation where the distribution of X conditional to Y has zero variance and the distribution of Y conditional to X has zero variance so that a bijective function f with f(X)=Y exists.\nThe Stuart-Kendall Tau-c coefficient is defined as:\nwhere\n\n# Significance tests\nWhen two quantities are statistically dependent, the distribution of \\tau is not easily characterizable in terms of known distributions.\nHowever, for \\tau_A the following statistic, z_A, is approximately distributed as a standard normal when the variables are statistically independent:\nwhere.\nThus, to test whether two variables are statistically dependent, one computes z_A, and finds the cumulative probability for a standard normal distribution at -|z_A|.\nFor a 2-tailed test, multiply that number by two to obtain the p-value.\nIf the p-value is below a given significance level, one rejects the null hypothesis (at that significance level) that the quantities are statistically independent.\nNumerous adjustments should be added to z_A when accounting for ties.\nThe following statistic, z_B, has the same distribution as the \\tau_B distribution, and is again approximately equal to a standard normal distribution when the quantities are statistically independent:\nwhere\nThis is sometimes referred to as the Mann-Kendall test.\n\n# Algorithms\nThe direct computation of the numerator n_c - n_d, involves two nested iterations, as characterized by the following pseudocode:\nnumer := 0\n**for** i := 2..N **do**\n**for** j := 1..(i − 1) **do**\nnumer := numer + sign(x[i] − x[j]) × sign(y[i] − y[j])\n**return** numer\nAlthough quick to implement, this algorithm is O(n^2) in complexity and becomes very slow on large samples.\nA more sophisticated algorithm built upon the [Merge Sort](https://bliptext.com/articles/merge-sort) algorithm can be used to compute the numerator in time.\nBegin by ordering your data points sorting by the first quantity, x, and secondarily (among ties in x) by the second quantity, y. With this initial ordering, y is not sorted, and the core of the algorithm consists of computing how many steps a [[Bubble Sort](https://bliptext.com/articles/bubble-sort)](https://bliptext.com/articles/bubble-sort) would take to sort this initial y. An enhanced [Merge Sort](https://bliptext.com/articles/merge-sort) algorithm, with O(n \\log n) complexity, can be applied to compute the number of swaps, S(y), that would be required by a [[Bubble Sort](https://bliptext.com/articles/bubble-sort)](https://bliptext.com/articles/bubble-sort) to sort y_i.\nThen the numerator for \\tau is computed as:\nwhere n_3 is computed like n_1 and n_2, but with respect to the joint ties in x and y.\nA [Merge Sort](https://bliptext.com/articles/merge-sort) partitions the data to be sorted, y into two roughly equal halves, and, then sorts each half recursive, and then merges the two sorted halves into a fully sorted vector.\nThe number of [Bubble Sort](https://bliptext.com/articles/bubble-sort) swaps is equal to:\nwhere and are the sorted versions of and, and characterizes the [Bubble Sort](https://bliptext.com/articles/bubble-sort) swap-equivalent for a merge operation.\nis computed as depicted in the following pseudo-code:\n**function** M(L[1..n], R[1..m]) **is**\ni := 1\nj := 1\nnSwaps := 0\n**while** i ≤ n **and** j ≤ m **do**\n**if** R[j] < L[i] **then**\nnSwaps := nSwaps + n − i + 1\nj := j + 1\n**else**\ni := i + 1\n**return** nSwaps\nA side effect of the above steps is that you end up with both a sorted version of x and a sorted version of y. With these, the factors t_i and u_j used to compute \\tau_B are easily obtained in a single linear-time pass through the sorted arrays.\n\n# Approximating Kendall rank correlation from a stream\nEfficient algorithms for calculating the Kendall rank correlation coefficient as per the standard estimator have time complexity.\nHowever, these algorithms necessitate the availability of all data to determine observation ranks, posing a challenge in sequential data settings where observations are revealed incrementally.\nFortunately, algorithms do exist to estimate approximations of the Kendall rank correlation coefficient in sequential settings.\nThese algorithms have O(1) update time and space complexity, scaling efficiently with the number of observations.\nConsequently, when processing a batch of n observations, the time complexity becomes O(n), while space complexity remains a constant O(1).\nThe first such algorithm presents an approximation to the Kendall rank correlation coefficient based on coarsening the joint distribution of the random variables.\nNon-stationary data is treated via a moving window approach.\nThis algorithm is simple and is able to handle discrete random variables along with continuous random variables without modification.\nThe second algorithm is based on Hermite series estimators and utilizes an alternative estimator for the exact Kendall rank correlation coefficient i.e. for the probability of concordance minus the probability of discordance of pairs of bivariate observations.\nThis alternative estimator also serves as an approximation to the standard estimator.\nThis algorithm is only applicable to continuous random variables, but it has demonstrated superior accuracy and potential speed gains compared to the first algorithm described, along with the capability to handle non-stationary data without relying on sliding windows.\nAn efficient implementation of the Hermite series based approach is contained in the R package package [hermiter](https://bliptext.com/articles/undefined).\n\n# Software Implementations","revisionCount":0},"hasMore":false,"nextCursor":null}

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