Exact goodness-of-fit tests for censored data

Exact goodness-of-fit tests for censored data Aurea Grané Statistics Department. Universidad Carlos III de Madrid. Abstract The statistic introduced in Fortiana and Grané (23, Journal of the Royal Statistical Society B, 65, 5-26) is modified so that it can be used to test the goodness-of-fit of a censored sample, when the distribution function is fully specified. Exact and asymptotic distributions of three modified versions of this statistic are obtained and exact critical values are given for different sample sizes. Empirical power studies show the good performance of these statistics in detecting symmetrical alternatives. Keywords: Goodness-of-fit, censored samples, maximum correlation, exact distribution, L-statistics. Introduction The usual way to measure system reliability is to test completed products or components, under conditions that simulate real life, until failure occurs. One may think that the more data available, the more confidence one will have in the reliability level, but this practice is often expensive and time-consuming. Hence, it is of special interest to analyze product life before all test units fail. This situation leads to censored samples, that are encountered naturally in reliability studies. The present paper is concerned on goodness-of-fit tests for this type of data with particular censoring schemes. Let y,...,y n be independent and identically distributed (iid) random variables with cumulative distribution function (cdf) F and consider the ordered sample y () <... < y (n). In the following we adopt the notation of Stephens and D Agostino (986). When some of the observations are missing the sample is said to be censored. If all the observations less than y (s) (s > ) are missing the sample is left-censored and if all the observations greater than y (r) (r < n) are missing, it is right-censored; in either case the sample is said to be singly-censored. If the observations are missing at both ends, the sample is doubly-censored. Censoring may occur for random values of s or r (Type I or time censoring) or for fixed values (Type II or failure censoring). In Fortiana and Grané (23) we proposed a goodness-of-fit statistic for complete samples to test the null hypothesis H : F(y) = F (y), where F (y) is a completely Work partially supported by Spanish projects MTM2-7323 (Spanish Ministry of Science and Innovation) and CCG-UAM/ESP-5494 (Autonomous Region of Madrid). Author s address: Statistics Department, Universidad Carlos III de Madrid, c/ Madrid, 26, 2893 Getafe, Spain. E mail: aurea.grane@uc3m.es

specified cdf, or equivalently to test that x,...,x n, where x i = F (y i ), are iid random variables uniformly distributed in the [, ] interval. The statistic is based on Hoeffding s maximum correlation (see the definition below) between the empirical cdf and the hypothesized. When there is no censoring, we found out that the test based on the proposed statistic can advantageously replace those of Kolmogorov-Smirnov, Cramér-von Mises and Anderson-Darling for a wide range of alternatives. Recently, Grané and Tchirina (2) studied the efficiency properties, in the Bahadur sense, of the test based on Q n and obtain its Bahadur local asymptotic optimality domains. The purpose of the present paper is to derive the exact distribution of Q n in the contextofcensoredsampling, withtheaimofobtainingatestthatperformsaswellas the uncensored one, and which provides a competitive tool for the reader. However this goal is only possible to achieve under some particular censoring schemes like Type I and Type II, and not under random censoring. Concerning the asymptotic distribution of Q n, the choice of Type I and Type II censoring schemes is again crucial as it allows the application of L-statistic-type results in the derivation of the asymptotics for Q n. Most goodness-of-fit statistics can be regarded as measures of proximity between two distributions: the empirical and the hypothesized. For instance, the Kolmogorov- Smirnov statistic is based on the supremum distance, whereas Cramér-von Mises and Anderson-Darling statistics use a weighted-l 2 distance. Another possibility is Kulback-Leibler information measure, that was used by Ebrahimi (2) for testing uniformity in a general set-up. However there are few references concerning censored samples. Lim and Park (27) adapted Kulback-Leibler information measure for Type II censored data, for testing normality or exponentiality, but not for uniformity. Moreover, tests based on Kulback-Leibler information measure (and also on entropy measures) present similar drawbacks both for complete and censored samples. It is not possible to obtain the exact distribution of the test statistic and critical values should be derived by Monte Carlo methods. Sometimes, the asymptotic distribution of the statistic is approximate and so the asymptotic critical values. Hence, when studying the power of the uniformity Q n -based test, in the context of censored samples, we compare it with the adapted versions (adapted, to the censoring schemes considered in this paper) of classical statistics, such as Kolmogorov-Smirnov, Cramérvon Mises and Anderson-Darling (Barr and Davidson 973, Pettitt and Stephens 976, Stephens and D Agostino 986). The remaining of paper proceeds as follows: in Section 2 the exact distributions of three modifications of the statistic are deduced and exact critical values are obtained for different sample sizes and different significance levels. In Section 3 we give some conditions under which the convergence to the normal distribution can be asserted. In Section 4 we study the power of the exact tests based on these statistics for five parametric families of alternative distributions with support contained in the [,] interval, where we conclude that the tests based on our proposals have a good performance in detecting symmetrical alternatives, whereas the tests based on the Kolmogorov-Smirnov, Cramér-von Mises and Anderson-Darling statistics are biased for some of these alternatives. In Section 5 we give an application in the engineering context. 2

2 Exact distributions We start by introducing Hoeffding s maximum correlation and recall how the Q n statistic is obtained in the case of complete samples. Let F and F 2 be two cdf s with second order moments. Hoeffding s maximum correlation between F and F 2, henceforth denoted by ρ + (F,F 2 ), is defined as the maximum of the correlation coefficients of bivariate distributions having F and F 2 as marginals: ρ + (F,F 2 ) = ( ) F σ σ (p)f 2 (p)dp µ µ 2, () 2 where F i is the left-continuous pseudoinverses of F i, µ i and σ 2 i are, respectively, the expectation and variance of F i, i =,2 (see, e.g., Cambanis, Simons, and Stout 976). Since ρ + (F,F 2 ) equals if and only if F = F 2 (almost everywhere) up to a scale and location change, it is a measure of proximity between two distributions and yields a goodness-of-fit test statistic when considering the empirical and hypothesized distributions. In Fortiana and Grané (23) we studied the test of uniformity based on Q n = s n /2 ρ + (F n,f U ), (2) where F n is the empirical cdf of n iid real-valued random variables, s n is the sample standard deviation and F U is the cdf of a uniform in [,] random variable. The exact distribution of Q n was obtained and its small and large sample properties were studied (see also Grané and Tchirina 2, where local Bahadur asymptotic optimality domains for Q n are obtained). In the following we deduce the expressions of the modified Q n statistic for singlyand doubly-censored samples and obtain their exact probability density functions (pdf s) under the null hypothesis of uniformity. For all the statistics we give tables of exact critical values for different sample sizes and different significance levels. 2. Right-censored samples Let y () <... < y (n) be the ordered sample. Suppose that the sample is rightcensored of Type I; the y i -values are known to be less than a fixed value y. The set of available transformed x i -values (x i = F (y i )) is then x () <... < x (r) < t, where t = F (y ). If the censoring is of Type II, there are again r values x (i), with x (r) the largest and r fixed. Proposition Under the null hypothesis of uniformity: (i) The modified Q n statistic for Type I right-censored data is r+ Q tn = a i x (i), (3) i= where a i = 6((2i )(r + ) n 2 )/(n 2 (r + )), for i r and a r+ = 6r(n 2 r 2 r)/(n 2 (r +)). 3

(ii) The modified Q n statistic for Type II right-censored data is 2Q rn = r a i x (i), (4) i= where a i = 6((2i )r n 2 )/(n 2 r), for i r and a r = 6(r )(n 2 r(r ))/(n 2 r). Proof: (i) For type I right-censored data, suppose t (t < ) is the fixed censoring value. This value can be added to the sample set (see Stephens and D Agostino 986), and the statistic can be calculated by using x (r+) = t. Note that it is possible to have r = n observations less than t, since when the value t is added the new sample has size n+. From formulas (2) and () we have that ( Q n = 2 Fn (p)f U (p)dp ) 2 x n. (5) Noticing that the pseudo-inverse of the empirical cdf is { Fn i x (p) = (i), n < p i n, i r, r x (r+), n < p, for p, the first summand of (5) is F n (p)f U (p)dp = r i= = 2n 2 i/n (i )/n r i= x (i) pdp+ n i=r+ i/n (i )/n x (r+) pdp (2i )x (i) + 2n 2(n2 r 2 )x (r+) and subtracting the (available) sample mean, the part of (5) between parenthesis is 2n 2 (r +) r i= ((2i )(r +) n 2 )x (i) + r(n2 r 2 r) 2n 2 (r +) x (r+). (ii) For Type II right-censored data the pseudo-inverse of the empirical cdf is { Fn i x (p) = (i), n < p i n, i r, r x (r), n < p, for p. Proceeding analogously, the first summand of (5) is Fn (p)f U (p)dp = r 2n 2 (2i )x (i) + 2 i= ( ) (r )2 n 2 x (r) and subtracting the (available) sample mean, the part of (5) between parenthesis is r 2n 2 ((2i )r n 2 )x r (i) + i= 4 (r ) 2n 2 r (n2 r(r ))x (r).

Under the null hypothesis, Q tn and 2 Q rn are linear combinations of selected order statistics from the [, ]-uniform distribution. Therefore their exact probability density functions can be obtained with the following algorithm, proposed by Dwass (96), Matsunawa (985) and Ramallingam (989). For Type I right-censored data, let r+ b i = a l = 6 n 2(2i i2 )+ 6 (i ), i =,2,...,r +, r + l=i let k be the number of distinct non-zero b i s, and (ν,...,ν k ) be the corresponding multiplicities of (b,...,b k ). Defining on C the functions: k ) G(s) = (s+ bj νj j=, G l (s) = ( s+ b l ) νl G(s), l =,2,...,k, the exact pdf of Q tn statistic, under H, is given by f Q tn (s) = k ν l l= m= sign(b l )C l,m χ ( s b l ) ) ( χ ( sbl s m s ) n m /B(m,n m+) b l where χ(x) is the indicator of the interval [x > ], B(a,b) is the Beta function, k C l,m = (b j ) ν j C l,m, C l,m = G(ν l m) l ( /b l ), (ν l m)! j= and G (j) l denotes the j-th derivative of G l. Analogously, for Type II right-censored data, the pdf of 2 Q rn is found by applying the previous algorithm, taking into account that in this case b i = r l=i a l = 6 n 2(2i i2 )+ 6 (i ), i =,2...,r. r Remark For left-censored data, note that from the r largest observations one can compute the values x (i) = x (n+ i), for i =,...,r, so that the sample becomes right-censored. In Type I censoring the left-censoring fixed value converts to t = t, to be used as the right-censoring fixed point. Mathematica programs implementing this algorithm are available from the author. As an illustration of the application, critical values for 5% and 2.5% significance levels are computed to test the null hypothesis of uniformity. They are reproduced in Table. In order to show the effect of right-censoring on the distribution of Q n, in Figure wedepictthedensityfunctionsofq n (completesamples), Q tn (TypeIright-censored data) and 2 Q rn (Type II right-censored data), for different p =.9,.8,.7 proportions of data in samples of size n = 2. 5

Figure : Density functions for (a) Q tn (Type I right-censored data) and (b) 2 Q rn (Type II right-censored data), for different p =.9,.8,.7 proportions of data in samples of size n = 2. 4 3.5 3 p=.7 p=.8 p=.9 p= (no censoring) 4 3.5 3 p=.7 p=.8 p=.9 p= (no censoring) 2.5 2.5 2 2.5.5.5.5.5.5 2.5.5 2 (a) (b) Table : Lower- and upper-tail critical values of Q tn and 2 Q rn for p proportions of data in the sample. 5% significance level 2.5% significance level p n = n = 2 n = 3 n = n = 2 n = 3.3.222.4577.3563.3467.4288.275.692.634.39.4778.376.3828.4.3566.6455.526.5568.633.4925.2874.859.459.685.554.5952.5.4867.7344.682.682.7788.635.47.8725.688.797.728.7239.6.598.7228.895.767.94.6853.542.8326.7356.876.845.763.7.675.695.8935.6554.9937.6465.5939.6875.8243.724.9335.767.8.6994.45.972.4956.25.53.6278.45.858.544.969.556.9.6337.368.8547.258.9443.2876.5789.735.8.287.967.3886 critical values for Q tn 5% significance level 2.5% significance level p n = n = 2 n = 3 n = n = 2 n = 3.3.999.678.274.25.3659.86.684.348.224.3423.372.294.4.222.4577.448.462.553.4284.692.634.38.595.4938.5334.5.3566.6455.666.633.7267.5943.2874.859.5359.7478.665.696.6.4867.7344.753.7.872.6773.47.8725.676.89.85.764.7.598.7228.8579.6983.9722.67.542.8326.7857.7784.998.7364.8.675.695.94.5876.45.5666.5939.6875.8493.6446.962.64.9.6994.45.8993.388.9789.3889.6278.45.8469.48.9367.46 critical values for 2 Q rn 2.2 Doubly-censored samples Let x (s) <... < x (r), with < s < r < n, be the available x i -values from a Type II doubly-censored sample. Proposition 2 Under the null hypothesis of uniformity, the modified Q n statistic for Type II doubly-censored data is 2Q sr,n = r a i x (i), (6) i=s 6

where a s = 6(s n 2 /(r s + ))/n 2, a i = 6((2i ) n 2 /(r s + ))/n 2, for s+ i r and a r = 6(n 2 (r ) 2 n 2 /(r s+))/n 2. Proof: Since in this case the pseudo-inverse of the empirical cdf is x (s), < p s Fn n, (p) = i x (i), n < p i n, s+ i r, r x (r), n < p, for p, the first summand of (5) is equal to F n (p)f U (p)dp = = s i= i/n (i )/n 2n 2sx (s) + 2n 2 x (s) pdp+ r i=s+ r i=s+ i/n (i )/n x (i) pdp+ n i=r i/n (i )/n (2i )x (i) + ( n 2 2n 2 (r ) 2) x (r). x (r) pdp Subtracting the (available) sample mean, the part of (5) between parenthesis is [ ( n 2 ) r ( n 2 ) ( ] 2n 2 s x (s) + (2i ) x (i) + n 2 (r ) 2 n 2 )x (r). r s+ r s+ r s+ i=s+ For Type II doubly-censored data, the pdf of 2 Q sr,n is found by applying the algorithm described in Section 2., taking into account that now { r 6 s( s), i = s, b i = a l = n 2 6 l=i (2i i 2 )+ 6 n 2 r s+ (i s), i = s+...,r. Analogously, it is possible to obtain tables of critical values for different values of r and s and different sample sizes. A Mathematica program implementing this algorithm is available from the author. As an illustration of its application, critical values for 5% and 2.5% significance levels are computed to test the null hypothesis of uniformity, for symmetric double-censoring, where p = r/n, q = s/n and p = q. They are reproduced in Table 2. Concerning to the Mathematica programs implementing the pdf s of Q tn, 2 Q rn and 2 Q sr,n, it should be mentioned that the original formula (2.3) of Ramallingam (989) for the pdf consists on a sum of terms, containing indicator functions of overlapping intervals. There we obtain an alternative expression taking disjoint intervals which saves computational resources in calculating the critical values. If k is the number of distinct non-zero b i s and we consider these coefficients ordered in ascending order, b () <... < b (k), then the pdf is defined by parts over the partition < b () <... < b (k), if all the b i s are positive or either over the partition b () <... < b (k) if there are negative b i s. In all cases, the support of these pdf s is determined by the interval [min{,b () },b (k) ]. 2.3 Expectation and variance In this Section we obtain expressions for the exact expectation and variance of 2 Q rn and 2 Q sr,n underthenullhypothesisofuniformity. Inmatrixnotation, thesestatistics 7

Table 2: Lower- and upper-tail critical values of 2 Q sr,n for different values of p = r/n and q = s/n, where p = q. n = p 5% sig. level 2.5% sig. level.8.4458.352.3798.3978.9.6994.45.6278.45 n = 2 p 5% sig. level 2.5% sig. level.8.5297.458.479.265.85.658.25.66.2535.9.7627.2378.736.275.95.8547.258.8.287 n = 3 p 5% sig. level 2.5% sig. level.8.577.752.5278.253.833.6498.95.672.63.867.7239.59.6824.89.9.792.736.757.248 can be written as 2Q rn = a x r, (7) where x r = (x (),...,x (r) ), a = (a,...,a r ), with a i = 6((2i )r n 2 )/(n 2 r), for i r and a r = 6(r )(n 2 r(r ))/(n 2 r), 2Q sr,n = a x sr, (8) where x sr = (x (s),...,x (r) ), a = (a s,...,a r ), with a s = 6(s n 2 /(r s + ))/n 2, a i = 6((2i ) n 2 /(r s+))/n 2, for s+ i r and a r = 6(n 2 (r ) 2 n 2 /(r s+))/n 2. It is straightforward to prove that when s = and r = n the previous statistics coincide with the Q n statistic for complete samples (see Proposition 3 of Fortiana and Grané 23). Proposition 3 Under the null hypothesis of uniformity the exact expectation and variance of 2 Q rn are given by E( 2 Q rn H ) = (3n2 +r 2r 2 )(r ) n 2, var( 2 Q rn H ) = a C r a, (n+) where a is the vector of coefficients of (7) and matrix C r = (c ij ) i,j r is defined by the covariances c ij = cov((x (i),x (j) ) H ) = {(n+)min(i,j) ij}, i,j r. (n+2)(n+) 2 Proposition 4 Under the null hypothesis of uniformity the exact expectation and variance of 2 Q sr,n are given by E( 2 Q sr,n H ) = var( 2 Q sr,n H ) = a C sr a, ( (+3(n r) 3n 2 n 2 4(n r) 2 )(n r) (n+) + (3n 2 )r +3r 2 2r 3), 8

where a is the vector of coefficients of (8) and matrix C sr = (c ij ) s i,j r, with covariances c ij defined as in Proposition 3, but for s i,j r. Proof: (of Propositions 3 and 4). Formulae for the expectation and variance of x r under the null can be found in David (98). Expressions for the expectations and variances of 2 Q rn and 2 Q sr,n are obtained from (7) and (8), respectively, after some easy but tedious ( computations. For example, to get the expectation of 2 Q rn substitute E(x r H ) = n+,..., r n+) for xr in formula (7). 3 Asymptotic distributions In this Section we give conditions under which the asymptotic normality of Q tn, 2Q rn and 2 Q sr,n can be established. With the same notation of Section 2 and under the null hypothesis of uniformity: Proposition 5 If r = o(n), the statistic 2 Q rn is asymptotically normally distributed, in the sense of sup P( 2 Q rn < t) Φ µn,σ n (t), (n ), <t< where Φ µn,σ n is the cdf of a normal random variable with expectation and variance µ n = (3n2 +r 2r 2 )(r ) n 2, (n+) σn 2 6(r +) ( = 5n 4 5n 4 (n+) 2 (2r ) 5n 2 (r )r 2 +r 2 (6r 3 9r 2 +r +) ). r Proof: The proof is based on Theorem 4.4 of Matsunawa (985). Since the assumptions of that theorem hold, to assert the convergence of 2 Q rn to the normal distribution we must prove that max i r b i (n+)σ n, (n ), (9) where coefficients b i s were defined as b i = 6 (2i i 2 )+ 6 n 2 r (i ) for i =,2...,r. From formulas (4.6) and (4.7) of Matsunawa (985), we obtain σ 2 n = (n+) 2 r i= µ n = n+ r i= b i = (3n2 +r 2r 2 )(r ) n 2, (n+) b 2 i = 6(r +)(5n4 (2r ) 5n 2 (r )r 2 +r 2 (6r 3 9r 2 +r +)) 5n 4 (n+) 2. r If r = o(n), condition (9) is fulfilled, since max b i max i r i r ( 6 n 2(i )2 + 6 r (i ) ) ( r = 6(r ) n 2 + ). r 9

Corollary Under the assumptions of Proposition 5, an analogous result can be established for the statistic Q tn with conditional mean and variance µ n = (3n 2 r 3r 2 2r 3 r)/(n 2 (n+)), σ 2 n = 6r(5n 2 (2r +) 5n 2 r(r +) 2 +(r +) 2 (6r 3 +9r 2 +r ))/(5n 4 (n 2 +)(r +)). Proof: The proof is analogous to that of Proposition 5, but taking into account that for Type I right-censored data coefficients b i s are b i = 6 (2i i 2 )+ 6 n 2 r+ (i ) for i =,2,...,r +. Proposition 6 If r = o(n), the statistic 2 Q sr,n, where s = n r, is asymptotically normally distributed, in the sense of sup P( 2 Q sr,n < t) Φ µn,σ n (t), (n ), <t< where Φ µn,σ n is the cdf of a normal random variable with expectation and variance µ n = σ 2 n = ( (+3(n r) 3n 2 n 2 4(n r) 2 )(n r)+(3n 2 )r +3r 2 2r 3), (n+) 6 ( 9n 6 5n 4 (n+) 2 4n 5 (2+37r)+n 4 (9+44r +47r 2 ) (n 2r +) n 3 (3+34r +9r 2 +76r 3 )+n 2 (+8r +47r 2 +94r 3 +64r 4 ) +2r(+2r +5r 2 +4r 3 +69r 4 +8r 5 )+ n(+4r +6r 2 +3r 3 +54r 4 +258r 5 ) ). Proof: To prove the convergence of 2 Q sr,n to the normal distribution is equivalent to proving that (see Theorem 4.4. of Matsunawa 985) max s i r b i (n+)σ n, (n ). () In the case of doubly-censored samples, coefficients b i s were defined as { 6 s( s), i = s, n 2 b i = Introducing s = n r, we have that max b i = max b i = s i r s+ i r 6 (2i i 2 )+ 6 n 2 r s+ (i s), i = s+...,r. 6 n 2 (2r n+) max s+ i r (i ) 2 (2r n+)+n 2 (i n+r). The function inside the absolute value is a polynomial of second order in i with a negative leading term. If i takes values from to n, this function is always positive and the maximum is attained at i = [+n 2 /(2(2r n+))], where [ ] denotes the nearest integer. However, since i takes values from s+ to r, it is possible that the maximum is attained at a certain i < i. In fact, we have that if r < i max s i r b i = b r < b i, if r i max s i r b i = b i.

In both cases it holds that max b i b i = 3 ( 4+5n 2 +2r +8r 2 4n(2+3r) ) s i r 2(n 2r +) 2. Expressions for the expectation and variance are obtained after applying formulas (4.6) and (4.7) of Matsunawa (985): ( ) ( ) µ n = r sb s + b i, σn 2 r = n+ (n+) 2 sb 2 s + b 2 i. i=s+ Finally condition () is fulfilled since σ 2 n = O(/n). i=s+ Remark 2 Note that in Propositions 5 and 6 the expectation of the limit distribution istheexactexpectationofthestatistic. Asymptoticcriticalvaluesfor 2 Q rn and 2 Q sr,n can be computed from the limit distribution using the corresponding asymptotic variance given in Propositions 5 and 6, or either the corresponding exact variance given in Propositions 3 and 4 (see Theorem 4.3 of Matsunawa 985). In Table 3 we show the relative error (percentage of its absolute value) with respect to the 5%-exact critical values of 2 Q rn in either situation, for several p proportions of data in a sample of size n = 3. Table 3: Relative error(percentage of its absolute value) of the 5%-asymptotic critical values of 2 Q rn computed from the limit distribution using (a) the exact variance of the statistic and (b) the asymptotic variance of the statistic, with respect to the exact critical values, for p proportions of data in a sample of size n = 3. (a) exact (b) asymp. p lower upper lower upper.3 3.69% 3.48% 29.73%.47%.4 6.73% 2.3% 22.78% 3.9%.5 3.39%.46% 9.93% 6.8%.6.47%.78% 8.76% 8.2%.7.25%.2% 8.54%.45%.8.56%.32% 9.2% 3.%.9.95%.86% 2.9% 4.4% 4 Power study and comparisons In this section we study the power of the tests based on 2 Q rn and 2 Q sr,n for a set of five parametric families of alternative distributions with support contained in the [,] interval. They have been chosen so that either the mean or the variance differs from those of the null distribution, which in each case is obtained for a particular value of the parameter. A. Lehmann alternatives. Asymmetric distributions with cdf F (x) = x, for x and >.

A2. Centered distributions having a U-shaped pdf, for (, ), or wedge-shaped pdf, for >, whose cdf is given by { 2 F (x) = (2x), x /2, 2 (2( x)), /2 x. A3. Compressed uniform alternatives in the [, ] interval, for < /2. A4. Centered distributions with parabolic pdf f (x) = + (6x( x) ), for x and 2. A5. Centered distributions with pdf given by 6 f (x) = 3/2 ) exp{(6x( x) )}, πe erf( /2 for x and >, where erf(x) = 2 π x e t2 dt is the error function. The power of the tests increases with the proportion of data in the sample. To illustrate this finding in Figure 2, we depicted the power functions of the 5% significance level test based on 2 Q rn, for p =.4,.6,.8 proportions of data in the sample. For each value of the parameter, the power was estimated from N = simulated samples of size n = from alternatives A A3 as the relative frequency of values of the statistic in the critical region. For each family we have taken 3 different values of the corresponding parameter. We used the exact critical values listed in Table. Figure 2: Power functions of the 5% significance level test based on 2 Q rn, for different p proportions of data in samples of size n =, for (a) A alternative, (b) A2 alternative and (d) A3 alternative..9.8 p=.4 p=.6 p=.8.9.8 p=.4 p=.6 p=.8.9.8 p=.4 p=.6 p=.8.7.7.7.6.6.6.5.5.5.4.4.4.3.3.3.2.2.2....5 2 2.5 3 3.5 4.5 2 2.5 3 3.5 4.5..5.2.25.3.35.4 (a) (b) (c) We have compared the power of the test based on 2 Q rn with those based on classical statistics such as the Kolmogorov-Smirnov, Cramér-von Mises and Anderson- Darling. Modified versions of these statistics for censored samples, as well as their critical values, can be found in Barr and Davidson (973), Pettitt and Stephens (976) and also in Stephens and D Agostino (986). Figure 3 contains the power 2

functions obtained from N = Type II right-censored samples of size n = 25 and a proportion of data of p =.8 for A A5 alternatives. For each family we have taken 3 different values of the corresponding parameter. We have denoted by 2D rn, 2 rn and 2 A 2 rn the modified versions of the Kolmogorov-Smirnov, Cramér-von Mises and Anderson-Darling statistics, respectively. Sample size and data proportion values of n = 25 and p =.8 were chosen so that the critical values reproduced in Stephens and D Agostino (986) were appropriate for comparison. For the test based on 2 Q rn we computed the exact critical regions. From Figure 3 we can observe the good performance of the 2 Q rn statistic in detecting symmetrical alternatives. Figure 3: Power functions of the 5% significance level tests based on 2 Q rn, 2 D rn, 2 rn and 2 A 2 rn for a p =.8 proportion of data in a sample of size n = 25, for (a) A alternative, (b)-(c) A2 alternative, (d) A3 alternative, (e) A4 alternative and (f) A5 alternative..9.8.7.6.9.8.7.6 Q D A 2.9.8.7.6 Q D A 2.5.5.5.4.4.4.3 2 Q r n D.2. A 2.5 2 2.5 3 3.5 4.3.2...2.3.4.5.6.7.8.9.3.2..5 2 2.5 3 3.5 4 (a) (b) (c).9.8.7.6.9.8.7.6 Q D A 2.9.8.7.6 Q D A 2.5.5.5.4.4.4.3 2 Q r n D.2. A 2.5..5.2.25.3.35.4.3.2..5.5.5.3.2..5.5 2 (d) (e) (f) We also compared the power of the test based on 2 Q sr,n with those based on the Cramér-von Mises and Anderson-Darling statistics. Critical values for the modified versions of these statistics for doubly-censored samples were computed by Pettitt and Stephens (976). Figure 4 contains the power functions obtained from N = Type II doubly-censored samples of size n = 2 and p = r/n =.9 (q = p, s = q/n) for A A5 alternatives. Once more, for each family we have taken 3 different values of the corresponding parameter. We have denoted by 2 rs,n and 2A 2 rs,n themodifiedversionsofthecramér-vonmisesandanderson-darlingstatistics, respectively. For the test based on 2 Q sr,n we computed the exact critical regions, whereas for 2 rs,n and 2 A 2 rs,n we considered the asymptotical ones, since Pettitt and 3

Stephens (976) concluded that the distributions of these statistics converge quickly to the asymptotical ones. From Figure 4 we can observe the good performance of the 2Q sr,n statistic in detecting symmetrical alternatives. Note also that the modified versions of the Cramér-von Mises and Anderson-Darling statistics are biased for A2 A5. Figure 4: Power functions of the 5% significance level tests based on 2 Q sr,n, 2 sr,n and 2 A 2 sr,n for n = 2 and p =.9, for (a) A alternative, (b) A2 alternative, (c) A3 alternative, (d) A4 alternative and (e) A5 alternative..9.8.7.9.8.7 Q 2 sr,n 2 sr,n 2 A 2 sr,n.9.8.7.6.6.6.5.5.5.4.4.4.3 Q 2 sr,n.2 2 sr,n. A 2 2 sr,n.5 2 2.5 3 3.5 4.9.3.2..2.4.6.8.2.4.6.8.3 Q 2 sr,n.2 2 sr,n. A 2 2 sr,n.5..5.2.25.3 (a) (b) (c).9.8.7 Q 2 sr,n 2 sr,n 2 A 2 sr,n.8.7 Q 2 sr,n 2 sr,n 2 A 2 sr,n.6.6.5.5.4.4.3.3.2.2.. 2.5.5.5 (d).5.5 2 (e) 5 Applications In the context of reliability analysis, the Weibull distribution is perhaps the most widelyusedlifetimedistributionmodel. Thetestbasedon 2 Q rn turnsouttobeuseful in detecting the Weibull family of distributions from the standard exponential. We illustrate this behavior in Figure 5, where we depict the power functions obtained from N = Type II right-censored samples of size n = 25 and p =.8 to test the null hypothesis of standard exponentiality versus the alternative of a Weibull distribution with scale parameter λ = and shape parameter >. These powe curves are evaluated on 3 different values of the parameter. Note the good performance of the test based on 2 Q rn in front of the classical ones based on Kolmogorov-Smirnov, Cramér-von Mises and Anderson-Darling. 4

Figure 5: Power functions of the 5% significance level goodness-of-fit tests based on 2Q rn, 2 D rn, 2 rn and 2 A 2 rn for a p =.8 proportion of data in a sample of size n = 25, to detect the Weibull alternative from the standard exponential..9.8.7.6.5.4 2.3 Q r,n D 2 2 r,n.2 2 r,n. 2 A 2 r,n.5.5 2 2.5 3 Concluding remarks We adapt the goodness-of-fit test based on Q n, introduced in Fortiana and Grané (23), for censored samples. We give tables of exact critical values for different sample sizes and significance levels making these tests easy to implement. The tests based on the modifications of Q n are consistent for all the families of alternatives studied, and are more powerful than those based on classical statistics, such as the Kolmogorov-Smirnov, Cramér-von Mises and Anderson-Darling statistics in detecting symmetrical alternatives. References Barr, D. R. and Davidson, T. (973). A Kolmogorov-Smirnov test for censored samples. Technometrics 5, 739 757. Cambanis, S., Simons, G. and Stout, W. (976). Inequalities for E k(x,y) when the marginals are fixed. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 36, 285 294. David, H. A. (98). Order statistics (2nd ed.). New York: John Willey & Sons, Inc. Dwass, M. (96). The distribution of linear combinations of random divisions of an interval. Trabajos de Estadística e Investigación Operativa 2, 7. Ebrahimi, N. (2). Testing for uniformity of the residual life time based on dynamic Kullback-Leibler information. Annals of the Institute of Statistical Mathematics 53 (2), 325 337. Fortiana, J. and Grané, A. (23). Goodness of fit tests based on maximum correlations and their orthogonal decompositions. Journal of the Royal Statistical Society B 65 (), 5 26. Grané, A. and Tchirina, A. (2). Asymptotic properties of a goodnessof-fit test based on maximum correlations. To appear in Statistics. 5

DOI:.8/233888.2.58879. Lim, J. and Park, S. (27). Censored Kullback-Leibler information and goodnessof-fit test with type ii censored data. Journal of Applied Statistics 34, 5 64. Matsunawa, T. (985). The exact and approximate distributions of linear combinations of selected order statistics from a uniform distribution. Annals of the Institute of Statistical Mathematics 37, 6. Pettitt, A. N. and Stephens, M. A. (976). Modified Cramér-von Mises statistics for censored data. Biometrika 63 (2), 29 298. Ramallingam, T. (989). Symbolic computing the exact distributions of L statistics from a uniform distribution. Annals of the Institute of Statistical Mathematics 4, 677 68. Stephens, M. A. and D Agostino, R. B. (Eds.) (986). Goodness of fit Techniques, New York. Marcel Dekker, Inc. 6