Asymptotic properties of Lee distance

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Distances on permutations are often convenient tools for analyzing and modeling rank data. They measure the closeness between two rankings and can be very useful and informative for revealing the main structure and features of the data. In this paper, some statistical properties of the Lee distance are studied. Asymptotic results for the random variable induced by Lee distance are derived and used to compare the Distance-based probability model and the Marginals model for complete rankings. Three rank datasets are analyzed as an illustration of the presented models.

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The work of the first author was supported by the Support Program of Bulgarian Academy of Sciences for Young Researchers under Grant 17-95/2017. The work of the second author was supported by the National Science Fund of Bulgaria under Grant DH02-13.

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Correspondence to Nikolay I. Nikolov.



In order to prove Theorem 3, let’s consider the random variables \(D_{N,k}=d_{L}\left( \pi ,e_{N}\right) \), where \(k=1,2,\ldots ,N\) and \(\pi \) is randomly selected from \({\mathbf {S}}_{N,k}=\left\{ \sigma \in {\mathbf {S}}_{N}: \sigma (N)=k\right\} \), i.e. \(\pi \sim Uniform({\mathbf {S}}_{N,k})\). Then, for fixed k,

$$\begin{aligned} D_{N,k}(\pi )=\sum \limits _{i=1}^{N}c_{N}(\pi (i),i)=\sum \limits _{i=1}^{N-1}c_{N}(\pi (i),i) + c_{N}(k,N)=\sum \limits _{i=1}^{N-1}\tilde{c}_{N}(\sigma (i),i) + c_{N}(k,N), \end{aligned}$$

where \(\sigma \in {\mathbf {S}}_{N-1}\) and for \(i,j=1,2,\ldots ,N-1\),

$$\begin{aligned} \sigma (i)= {\left\{ \begin{array}{ll} \pi (i), &{} \text{ if } \pi (i)<k\\ \pi (i)-1, &{} \text{ if } \pi (i)>k, \end{array}\right. } \qquad \tilde{c}_{N}(j,i)= {\left\{ \begin{array}{ll} c_{N}(j,i), &{} \text{ if } j<k \\ c_{N}(j+1,i), &{} \text{ if } j\ge k. \end{array}\right. } \end{aligned}$$

Lemma 1

Let \( \tilde{D}_{N-1}\left( \sigma \right) =\sum \nolimits _{i=1}^{N-1}\tilde{c}_{N}(\sigma (i),i)\), where \(\sigma \sim Uniform({\mathbf {S}}_{N-1})\) and \(\tilde{c}_{N}(\cdot ,\cdot )\) is as in (21). Then the distribution of \(\tilde{D}_{N-1}\) is asymptotically normal and the mean and variance of \(\tilde{D}_{N-1}\) are

$$\begin{aligned} {\mathbf {E}}\left( \tilde{D}_{N-1}\right)&= \displaystyle \frac{c_{N}(k,N)}{N-1}+\frac{N-2}{N-1}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] , \\ {\mathbf {Var}} \left( \tilde{D}_{N-1}\right)&= \displaystyle \frac{ \displaystyle N^{2} \left( c_{N}\left( k,N\right) \right) ^{2}- 2N\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] c_{N}\left( k,N\right) }{\left( N-2\right) \left( N-1\right) ^{2}} + \beta _{N-1}, \end{aligned}$$


$$\begin{aligned} \beta _{N-1} = {\left\{ \begin{array}{ll} \displaystyle \frac{N^{2}\left( N^{3}-2N^{2}+10N-12\right) }{48(N-1)^{2}}, &{}\quad \text{ for } N \text{ even } \\ \displaystyle \frac{\left( N+1\right) \left( N^{3}-3N^{2}+6N-6\right) }{48(N-2)}, &{}\quad \text{ for } N \text{ odd. } \end{array}\right. } \end{aligned}$$


From (6) of Theorem 1 and formulas (21) and (10), it follows that

$$\begin{aligned}&{\mathbf {E}}\left( \tilde{D}_{N-1}\right) {\mathop {=}\limits ^{(6)}}\frac{1}{N-1} \sum _{i=1}^{N-1}\sum _{j=1}^{N-1}\tilde{c}_{N}(i,j) {\mathop {=}\limits ^{(21)}}\frac{1}{N-1}\sum _{\begin{array}{c} i=1 \\ i\ne k \end{array}}^{N}\sum _{j=1}^{N-1}c_{N}(i,j)\\&\quad =\frac{1}{N-1}\sum _{i=1}^{N}\sum _{j=1}^{N}c_{N}(i,j)-\frac{1}{N-1} \sum _{i=1}^{N}c_{N}(i,N)-\frac{1}{N-1}\sum _{j=1}^{N}c_{N}(k,j)+\frac{c_{N}(k,N)}{N-1}\\&\quad {\mathop {=}\limits ^{(10)}}\frac{N}{N-1}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] -\frac{1}{N-1}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] -\frac{1}{N-1}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] +\frac{c_{N}(k,N)}{N-1}\\&\quad =\frac{c_{N}(k,N)}{N-1}+\frac{N-2}{N-1}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] . \end{aligned}$$

Using (7) of Theorem 1,

$$\begin{aligned} {\mathbf {Var}} \left( \tilde{D}_{N-1}\right)= & {} \frac{1}{N-2}\sum _{i=1}^{N-1}\sum _{j=1}^{N-1}\tilde{b}_{N}^{2}(i,j)=\frac{1}{N-2}\sum _{\begin{array}{c} i=1 \\ i\ne k \end{array}}^{N}\sum _{j=1}^{N-1}b_{N}^{2}(i,j), \quad \text{ where }\nonumber \\ b_{N}(i,j)= & {} c_{N}(i,j)- \sum _{\begin{array}{c} g=1 \\ g\ne k \end{array}}^{N}\frac{c_{N}(g,j)}{N-1}-\sum _{h=1}^{N-1}\frac{c_{N}(i,h)}{N-1}+\frac{1}{\left( N-1\right) ^{2}} \sum _{\begin{array}{c} g=1 \\ g\ne k \end{array}}^{N}\sum _{h=1}^{N-1}c_{N}(g,h), \nonumber \\ \end{aligned}$$

for \(i,j=1,2,\ldots ,N\). Simplifying expression (23) gives

$$\begin{aligned} b_{N}(i,j)=c_{N}(i,j)+\frac{c_{N}(i,N)+c_{N}(k,j)}{N-1}+\frac{c_{N}(k,N)}{\left( N-1\right) ^{2}}-\frac{N}{\left( N-1\right) ^{2}}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] . \end{aligned}$$

When N is even, the variance of \(\tilde{D}_{N-1}\) can be calculated by

$$\begin{aligned} {\mathbf {Var}} \left( \tilde{D}_{N-1}\right)&=\frac{1}{N-2}\sum _{\begin{array}{c} i=1 \\ i\ne k \end{array}}^{N}\left\{ \sum _{j=1}^{k-\frac{N}{2}}b_{N}^{2}(i,j)+\sum _{j=k-\frac{N}{2}+1}^{\frac{N}{2}}b_{N}^{2}(i,j)+\sum _{j=\frac{N}{2}+1}^{k}b_{N}^{2}(i,j)\right. \\&\quad \left. +\sum _{j=k+1}^{N-1}b_{N}^{2}(i,j)\right\} =\frac{1}{N-2}\left( Q_{1}+Q_{2}+Q_{3}+Q_{4}\right) , \end{aligned}$$

where the summation \(\sum _{j=l_{1}}^{l_{2}}=0\), if \(l_{1}>l_{2}\). Since the computations for \(Q_{1}\), \(Q_{2}\), \(Q_{3}\) and \(Q_{4}\) are similar, only the steps for \(Q_{1}\) are presented herein.

$$\begin{aligned} Q_{1}&=\sum _{\begin{array}{c} i=1 \\ i\ne k \end{array}}^{N}\sum _{j=1}^{k-\frac{N}{2}}b_{N}^{2}(i,j)=\sum _{j=1}^{k-\frac{N}{2}}\sum _{\begin{array}{c} i=1 \\ i\ne k \end{array}}^{N}b_{N}^{2}(i,j)= \sum _{j=1}^{k-\frac{N}{2}}\left\{ \sum _{i=1}^{j-1}b_{N}^{2}(i,j)+ \sum _{i=j}^{\frac{N}{2}}b_{N}^{2}(i,j)\right. \\&\quad \left. +\sum _{i=\frac{N}{2}+1}^{\frac{N}{2}+j-1}b_{N}^{2}(i,j)+\sum _{i=\frac{N}{2}+j}^{N}b_{N}^{2}(i,j)-b_{N}^{2}(k,j)\right\} =Q_{1}^{(1)}+Q_{1}^{(2)}+Q_{1}^{(3)}+Q_{1}^{(4)}-Q_{1}^{(5)}, \end{aligned}$$


$$\begin{aligned} Q_{1}^{(1)}&= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=1}^{j-1}b_{N}^{2}(i,j)= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=1}^{j-1} \left( j-i+\frac{i+(N-k+j)}{N-1}+B_{N}(k)\right) ^{2}, \\ Q_{1}^{(2)}&= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=j}^{\frac{N}{2}}b_{N}^{2}(i,j)= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=j}^{\frac{N}{2}} \left( i-j+\frac{i+(N-k+j)}{N-1}+B_{N}(k)\right) ^{2}, \\ Q_{1}^{(3)}&= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=\frac{N}{2}+1}^{\frac{N}{2}+j-1}b_{N}^{2}(i,j)= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=\frac{N}{2}+1}^{\frac{N}{2}+j-1} \left( i-j+\frac{N-i+(N-k+j)}{N-1}+B_{N}(k) \right) ^{2}, \\ Q_{1}^{(4)}&= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=\frac{N}{2}+j}^{N}b_{N}^{2}(i,j)= \sum _{j=1}^{k-\frac{N}{2}}\sum _{i=\frac{N}{2}+j}^{N} \left( N-i+j+\frac{N-i+(N-k+j)}{N-1}+B_{N}(k)\right) ^{2}, \\ Q_{1}^{(5)}&= \sum _{j=1}^{k-\frac{N}{2}}b_{N}^{2}(k,j)= \sum _{j=1}^{k-\frac{N}{2}} \left( N-k+j+\frac{N-k+(N-k+j)}{N-1}+B_{N}(k)\right) ^{2}, \end{aligned}$$

for \( B_{N}(k)=\frac{c_{N}(k,N)}{\left( N-1\right) ^{2}}-\frac{N}{\left( N-1\right) ^{2}}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] =\frac{4(N-k)-N^{3}}{4\left( N-1\right) ^{2}}\) and \(\sum _{i=l_{1}}^{l_{2}}=0\), if \(l_{1}>l_{2}\). The calculation of \(Q_{1}\) is completed by repeatedly using the formula

$$\begin{aligned} \sum _{i=1}^{n}\left( i-a\right) ^{2}=na^{2}+\frac{n(n+1)(2n+1-6a)}{6} \end{aligned}$$

for appropriate values of a and n.

The quantities \(Q_{2}\), \(Q_{3}\) and \(Q_{4}\) can be decomposed and calculated in a similar fashion as shown for \(Q_{1}\). The final result for the variance of \(\tilde{D}_{N-1}\), when N is even, is

$$\begin{aligned} {\mathbf {Var}} \left( \tilde{D}_{N-1}\right) = \displaystyle \frac{ \displaystyle 2N^{2} \left( c_{N}\left( k,N\right) \right) ^{2}- N^{3}c_{N}\left( k,N\right) }{2\left( N-2\right) \left( N-1\right) ^{2}} + \frac{N^{2}\left( N^{3}-2N^{2}+10N-12\right) }{48(N-1)^{2}}. \end{aligned}$$

The variance \({\mathbf {Var}} \left( \tilde{D}_{N-1}\right) \), when N is odd, can be obtained by decomposing it to four decomposable double sums and applying formula (25), as in the case when N is even.

From (24) and (2), it follows that

$$\begin{aligned} \displaystyle \max _{1 \le i,j \le N}b_{N}^{2}(i,j) \le \left( \left[ \frac{N}{2}\right] +\frac{\displaystyle \left[ \frac{N}{2}\right] +\left[ \frac{N}{2}\right] }{N-1}+\frac{\displaystyle \left[ \frac{N}{2}\right] }{\left( N-1\right) ^{2}}-\frac{N}{\left( N-1\right) ^{2}}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] \right) ^{2}. \end{aligned}$$

By using (22),

$$\begin{aligned} \displaystyle \frac{1}{N-1}\sum _{i=1}^{N-1}\sum _{j=1}^{N-1}\tilde{b}_{N}^{2}(i,j)=\frac{N-2}{N-1} {\mathbf {Var}} \left( \tilde{D}_{N-1}\right) \ge \frac{N-2}{N-1}\beta _{N-1}= N^{3}\left( \frac{1}{48}+O\left( \frac{1}{N}\right) \right) , \end{aligned}$$

where \(\lim _{N \rightarrow \infty }O\left( \frac{1}{N}\right) =0\). Therefore,

$$\begin{aligned} \lim _{N \rightarrow \infty } \frac{ \max _{1 \le i,j \le N-1}\tilde{b}_{N}^{2}(i,j)}{ \frac{1}{N}\sum _{i=1}^{N-1}\sum _{j=1}^{N-1}\tilde{b}_{N}^{2}(i,j)}\le \lim _{N \rightarrow \infty } \frac{N^{2}\left( \frac{1}{16}+O\left( \frac{1}{N}\right) \right) }{N^{3}\left( \frac{1}{48}+O\left( \frac{1}{N}\right) \right) }=0, \end{aligned}$$

i.e. the condition (8) of Theorem 1 is fulfilled and the distribution of \(\tilde{D}_{N-1}\) is asymptotically normal. \(\square \)

Proof (Proof of Theorem 3)

From (14), (19) and (15), it follows that

$$\begin{aligned} m_{ij}(\theta ,N)=\sum _{\pi (i)=j} \exp \left( \theta d(\pi ,e_{N})-\psi _{N}(\theta )\right) =\frac{(N-1)!\tilde{g}_{N-1}(\theta )}{N!g_{N}(\theta )}=\frac{1}{N}\frac{\tilde{g}_{N-1}(\theta )}{g_{N}(\theta )}, \end{aligned}$$

where \(g_{N}(\cdot )\) and \(\tilde{g}_{N-1}(\cdot )\) are the moment generating functions of \(D_{L}(\pi )\) and \(D_{i,j}(\sigma )\), for \(\pi \sim Uniform({\mathbf {S}}_{N})\) and \(\sigma \sim Uniform({\mathbf {S}}_{i,j})\). Since \(D_{i,j}\) depends on i and j only through \(c_{N}(i,j)\), the random variables \(D_{i,j}\) and \(D_{N,k}\) are identically distributed for \({k=N-c_{N}(i,j)}\). From Theorem 2 and Lemma 1, \(g_{N}(\cdot )\) and \(\tilde{g}_{N-1}(\cdot )\) can be approximated, so

$$\begin{aligned} m_{ij}(\theta ,N) \frac{N}{ \exp \left( \theta \mu + \displaystyle \frac{\theta ^{2}\nu ^{2}}{2}\right) } \xrightarrow [N \rightarrow \infty ] \displaystyle 1, \end{aligned}$$

where \(\mu ={\mathbf {E}}\left( D_{i,j}\right) -{\mathbf {E}}(D_{L})\) and \(\nu ^{2}={\mathbf {Var}}\left( D_{i,j}\right) -{\mathbf {Var}}(D_{L})\).

According to Lemma 1,

$$\begin{aligned} {\mathbf {E}}\left( D_{i,j}\right)&= \displaystyle \frac{c_{N}(i,j)}{N-1}+\frac{N-2}{N-1}\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] +c_{N}(i,j),\\ {\mathbf {Var}} \left( D_{i,j}\right)&= \displaystyle \frac{ \displaystyle N^{2} \left( c_{N}\left( i,j\right) \right) ^{2}- 2N\left[ \frac{N+1}{2}\right] \left[ \frac{N}{2}\right] c_{N}\left( i,j\right) }{\left( N-2\right) \left( N-1\right) ^{2}} + \beta _{N-1}. \end{aligned}$$

The values of \(\mu \) and \(\nu ^{2}\) are obtained by combining the results above with formulas (10) and (12). \(\square \)

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Nikolov, N.I., Stoimenova, E. Asymptotic properties of Lee distance. Metrika 82, 385–408 (2019).

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  • Lee distance
  • Rank data
  • Distance-based models
  • Marginals model
  • Asymptotic distribution