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Data transformations and goodness-of-fit tests for type-II right censored samples


We suggest several goodness-of-fit (GOF) methods which are appropriate with Type-II right censored data. Our strategy is to transform the original observations from a censored sample into an approximately i.i.d. sample of normal variates and then perform a standard GOF test for normality on the transformed observations. A simulation study with several well known parametric distributions under testing reveals the sampling properties of the methods. We also provide theoretical analysis of the proposed method.

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Correspondence to Bernhard Klar.

Additional information

Simos G. Meintanis: On sabbatical leave from the University of Athens.



In this appendix we shall investigate the reasons underlying the eventual validity of the Chen–Balakrishnan transformation. In doing so, first in Sect. 7.1 of the appendix we report results on the process corresponding to goodness-of-fit testing for the normal distribution with estimated parameters. Then in Sects. 7.2 and 7.3 of the appendix we study in detail the process produced by the Chen–Balakrishnan transformation and compare it with the process in Sect. 7.1 both theoretically and by simulation. The figures referred to in this appendix can be retrieved from the technical report (TR) at arXiv:1312.3078 [stat.ME].

The empirical process under normality

Suppose that \(Z_j, \ j=1,\ldots ,n\), are iid normal with unknown mean and variance. Then most standard goodness-of-fit tests are merely functionals of the empirical process

$$\begin{aligned} \hat{\alpha }_n(t)&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ \varPhi \left( \frac{Z_j-\bar{Z}}{s_Z} \right) \le t\right\} - t \right] \\&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ U_j \le \varPhi \left( \bar{Z} + s_Z \; \varPhi ^{-1}(t) \right) \right\} - t \right] , \end{aligned}$$

where \(\bar{Z}\) and \(s^{2}_{Z}\) are the sample mean and sample variance of \(Z_1,\ldots ,Z_n\), and \(U_j=\varPhi (Z_j)\) and \(t \in [0,1]\). This process has been studied by Durbin (1973) and showed that under regularity conditions,

$$\begin{aligned} \hat{\alpha }_n \Rightarrow \alpha \end{aligned}$$

where \(\alpha \) is a centered Gaussian process with covariance function

$$\begin{aligned} C(\alpha (s),\alpha (t))&= \min (s,t) - st - \varphi (\varPhi ^{-1}(s)) \varphi (\varPhi ^{-1}(t)) \\&- \frac{1}{2} \varPhi ^{-1}(s) \varphi (\varPhi ^{-1}(s)) \varPhi ^{-1}(t) \varphi (\varPhi ^{-1}(t)), \end{aligned}$$

where \(\varPhi ^{-1}\) and \(\varphi \) are the quantile and density function of the standard normal distribution (We note that Loynes (1980) extended the analysis from the iid setting to the case of generalized linear models). Clearly the process \(\alpha _n\) is identical to the process involved in the Chen–Balakrishnan transformation only in the case of testing for normality with estimated parameters.

The empirical process under non-normality

In this section, we consider iid random variables \(X_j\) with DF \(\mathcal{{F}}_{\vartheta }(x)\) (assumed to be continuous and strictly increasing) and the standardized quantile residuals \(Z_j= \frac{Y_j-\bar{Y}}{s_Y}\) with \(Y_j= \varPhi ^{-1} \left( \mathcal{{F}}_{\hat{\vartheta }}(X_j) \right) \) (concerning the term standardized quantile residual, refer to Klar and Meintanis (2012), sec 2.1). We shall study the following empirical process based on the \(Z_j\):

$$\begin{aligned} \hat{\beta }_n(t)&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ \varPhi (Z_j) \le t \right\} - t \right] \\&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ \varPhi \left( \frac{Y_j-\bar{Y}}{s_Y} \right) \le t\right\} - t \right] \\&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ X_j \le \mathcal{{ F}}_{\hat{\vartheta }}^{-1} ( \varPhi (\bar{Y} + s_Y \, \varPhi ^{-1}(t)) ) \right\} - t \right] \\&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ U_j \le \mathcal{{F}}_{\vartheta } \left( \mathcal{{F}}_{\hat{\vartheta }}^{-1} \left( \varPhi (\bar{Y} + s_Y \, \varPhi ^{-1}(t)) \right) \right) \right\} - t \right] , \end{aligned}$$

where \(\mathcal{{ F}}_{\vartheta }^{-1}(p)\) denotes the quantile function of \(\mathcal{{ F}}_{\vartheta }(\cdot )\), and \(U_j = \mathcal{{F}}_{\vartheta }(X_j)\) are iid uniformly distributed on \([0,1]\). This is the empirical process actually produced by the Chen–Balakrishnan transformation. Now define \(c_Y(t)=\varPhi (\bar{Y} + s_Y \varPhi ^{-1}(t))\), and, similarly, \(c_N(t)=\varPhi (\bar{N} + s_N \varPhi ^{-1}(t))\), where \(N_j=\varPhi ^{-1}(U_j)\) are iid standard normal random variates, and \(\bar{N}\) and \(s_N^2\) are the arithmetic mean and sample variance of \(N_1,\ldots ,N_n\).

Then we can decompose the above process as (compare Chen 1991, pp 126-128)

$$\begin{aligned} \hat{\beta }_n(t) = \hat{\beta }_{n,1}(t) + \hat{\beta }_{n,2}(t) + \hat{\beta }_{n,3}(t), \end{aligned}$$


$$\begin{aligned} \hat{\beta }_{n,1}(t)&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ U_j \le \mathcal{{F}}_{\vartheta } \left( \mathcal{{F}}_{\hat{\vartheta }}^{-1} (c_Y(t)) \right) \right\} - \mathcal{{F}}_{\vartheta } \left( \mathcal{{F}}_{\hat{\vartheta }}^{-1} (c_Y(t)) \right) \right. \\&- I\left\{ U_j \le c_N(t) \right\} + c_N(t) \Big ],\\ \hat{\beta }_{n,2}(t)&= \frac{1}{\sqrt{n}} \sum _j \left[ I\left\{ U_j \le \varPhi (\bar{N} + s_N \, \varPhi ^{-1}(t)) \right\} - t \right] , \end{aligned}$$


$$\begin{aligned} \hat{\beta }_{n,3}(t)= \frac{1}{\sqrt{n}} \sum _j \left[ \mathcal{{F}}_{\vartheta } \left( \mathcal{{F}}_{\hat{\vartheta }}^{-1} (c_Y(t)) \right) - c_N(t) \right] . \end{aligned}$$

The first part \(\hat{\beta }_{n,1}(t)\) in decomposition (Sect. 7.1) is the difference of an empirical process and a random perturbation thereof, and should be \(o_P(1)\) under appropriate regularity conditions (see Chen 1991, p.128; Loynes (1980), Lemma 1; and Rao and Sethuraman 1975). To check this claim, we simulated a random sample of size \(n\) from a unit mean exponential distribution and computed \(\hat{\beta }_{n,1}(t), \ t \in [0,1]\), on the basis of an equidistant grid with spacing equal to 0.005. This was repeated \(B=10000\) times. We approximated the mean function \(E[\hat{\beta }_{n,1}(t)]\) and the standard deviation \(\sqrt{Var[\hat{\beta }_{n,1}(t)]}\) by the arithmetic mean and empirical standard deviation based on the \(B\) replications; Figure 4 in the TR shows the result for sample sizes \(n=10,40,160\) and \(640\). Clearly the mean function is nearly zero and decreases for increasing \(n\), while the standard deviation is small compared to the standard deviation of \(\hat{\beta }_n\) or \(\hat{\beta }_{n,2}\) (see below). The corresponding variance seems to converge to zero, but rather slowly, with a speed of convergence approximately equal to \(1/\sqrt{n}\).

The second part \(\hat{\beta }_{n,2}\) corresponds to the normal empirical process \(\hat{\alpha }_{n}\) in Sect. 7.1 of the appendix. Figure 5 in the TR shows the empirical mean function and standard deviation of \(\hat{\beta }_{n,2}\) for an underlying exponential distribution computed in the same way as for \(\hat{\beta }_{n,1}\) above. The mean function, which takes on much larger values than that of \(\hat{\beta }_{n,1}\), again converges to zero, whereas the variance function is nearly constant for \(n\ge 40\).

For the third part we have

$$\begin{aligned} \hat{\beta }_{n,3}(t)= \sqrt{n} \left[ \mathcal{{F}}_{\vartheta } \left( \mathcal{{F}}_{\hat{\vartheta }}^{-1} (\varPhi (\bar{Y} + s_Y \, \varPhi ^{-1}(t))) \right) - \varPhi (\bar{N} + s_N \, \varPhi ^{-1}(t)) \right] . \end{aligned}$$

In general, this process does not converge to zero in probability. However, the contribution of \(\hat{\beta }_{n,3}\) seems to be negligibly small in comparison to \(\hat{\beta }_{n,2}\) in many situations. Figure 6 in the TR shows the empirical mean function and standard deviation of \(\hat{\beta }_{n,3}\) for the exponential distribution, computed as above. The mean function is very small and goes to zero. The standard deviation is small compared to the standard deviation of \(\hat{\beta }_{n,2}\), and it converges, but not to zero. We stress that the crucial point for the behavior of \(\hat{\beta }_{n,3}\) is the coupling between the \(Y_j\)’s and the normal variates \(N_j\) which are both based on the original \(X_j\), the first computed by using \(\hat{\vartheta }\) while the second by using \(\vartheta \). In fact if we generate iid standard normal random variables \(\tilde{N}_j\) independent of the \(X_j\)’s and use them instead of the \(N_j\)’s, the mean function is small, but does not seem to converge to zero, and the variance is much larger, even larger than that of \(\hat{\beta }_{n,2}\).

From the above it follows that the values of the process \(\hat{\beta }_{n}\) will be eventually dominated by \(\hat{\beta }_{n,2}\), at least for large \(n\). This is documented in Figure 7 of the TR where the mean and standard deviation of all four processes are plotted for \(n=40\). Note that the standard deviations of \(\hat{\beta }_{n}\) (in red) and \(\hat{\beta }_{n,2}\) (in green) are nearly identical, and therefore, visually indistinguishable.

Further analysis of the process \(\varvec{\hat{\beta }}_{n,3}\)

To keep things simple, we assume in the following that \(\vartheta \in \varTheta \subset \mathbb {R}\). Let \(\vartheta _0\) denote the true parameter value, and define

$$\begin{aligned} N_j(\vartheta )&= \varPhi ^{-1}\left( \mathcal{{F}}_{\vartheta }\left( X_j \right) \right) , \\ \bar{N}(\vartheta )&= \frac{1}{n} \sum _{j=1}^n N_j(\vartheta ), \quad s_N^2(\vartheta ) \;=\; \frac{1}{n-1} \sum _{j=1}^n \left( N_j(\vartheta )-\bar{N}(\vartheta )\right) ^2. \end{aligned}$$

Then, \(N_j(\vartheta _0)=N_j, \bar{N}(\vartheta _0)=\bar{N}, s_N^2(\vartheta _0)=s_N^2\), and \(N_j(\hat{\vartheta })=Y_j, \bar{N}(\hat{\vartheta })=\bar{Y}, s_N^2(\hat{\vartheta })=s_Y^2\). Putting

$$\begin{aligned} h_t(\vartheta ) = \varPhi \left( \bar{N}(\vartheta ) + s_N(\vartheta )\cdot \varPhi ^{-1}(t) \right) , \end{aligned}$$

we obtain \(h_t(\vartheta _0)=c_N(t)\) and \(h_t(\hat{\vartheta })=c_Y(t)\). Thus, we can write

$$\begin{aligned} \hat{\beta }_{n,3}(t) = \sqrt{n} \left( g_t(\hat{\vartheta }) - g_t(\vartheta _0) \right) , \end{aligned}$$


$$\begin{aligned} g_t(\vartheta ) = \mathcal{{F}}_{\vartheta _0}\left( \mathcal{{F}}_{\vartheta }^{-1}\left( h_t(\vartheta ) \right) \right) . \end{aligned}$$

Assume now that \(\sqrt{n}(\hat{\vartheta }-\vartheta _0)=O_p(1)\). Then, by using the expansion

$$\begin{aligned} g_t(\hat{\vartheta }) = g_t(\vartheta _0) + (\hat{\vartheta }-\vartheta _0) \, g_t^{\prime }(\vartheta _0) + (\hat{\vartheta }-\vartheta _0)^2 \, g_t^{''}(\vartheta ^*)/2, \end{aligned}$$

with \(\vartheta ^*\) between \(\hat{\vartheta }\) and \(\vartheta _0\), and by omitting the quadratic term, we see that \(\hat{\beta }_{n,3}(t)\) can be approximated by

$$\begin{aligned} \mathring{\beta }_{n,3}(t) = \sqrt{n} \left( \hat{\vartheta }-\vartheta _0 \right) \, g_t^{\prime }\left( \vartheta _0\right) . \end{aligned}$$

Of course, the validity of such a Taylor expansion is not enough to justify the uniform convergence \(\sup _t |\hat{\beta }_{n,3}(t) - \mathring{\beta }_{n,3}(t)|=o_P(1)\). A sufficient condition would be Fréchet differentiability of \(g_t(\cdot )\) (see, e.g. van der Vaart and Wellner (2002), p. 373). However, since we do not intend to give rigorous theory here, this issue is not discussed in any detail. Further analysis of \(g_t^{\prime }\left( \vartheta _0\right) \) leads to the following result, the proof of which is omitted.

Lemma 7.1

Let \(\bar{W}\) and \(s_W^2\) denote the arithmetic mean and sample variance of the random variables \(W_{j0}:=W_j(\vartheta _0)\), with \(W_j(\vartheta ):=d N_j(\vartheta )/d\vartheta \), while \(r\) denotes the sample correlation coefficient of \(W_{10},\ldots ,W_{n0}\) and \(N_1,\ldots ,N_n\). Then,

$$\begin{aligned} g_t^{\prime }(\vartheta _0)&= \frac{\partial \mathcal{{F}}_{\vartheta _0}\left( F_{\vartheta _0}^{-1}(c_N(t)) \right) }{\partial x} \cdot \left( \frac{\partial \mathcal{{F}}_{\vartheta _0}^{-1}(c_N(t))}{\partial p} \cdot h_t^{\prime }(\vartheta _0) + \frac{\partial \mathcal{{F}}_{\vartheta _0}^{-1}(c_N(t))}{\partial \vartheta } \right) , \\ h_t^{\prime }(\vartheta _0)&= \varphi \left( \bar{N}+s_N \, \varPhi ^{-1}(t) \right) \cdot \left( \bar{W}+\varPhi ^{-1}(t) \, r \, s_W \right) , \end{aligned}$$

where \(\varphi (\cdot )\) denotes the density of the standard normal distribution.

Since \(N_1,\ldots ,N_n\) are iid standard normal variates, \(\bar{N} \rightarrow 0\) and \(s_N\rightarrow 1\) almost surely. Furthermore, \(\bar{W} \rightarrow \mu _W, s_W \rightarrow \sigma _W\), and \(r \rightarrow \rho \) a.s., where \((\mu _W,\sigma _W^2)\) are the mean and variance of \(W_{10}\), while \(\rho \) denotes the correlation coefficient of \(W_{10}\) and \(N_1\). Hence, the following approximation holds for the process in (7.3).

Lemma 7.2

The process \(\mathring{\beta }_{n,3}(t)\) can be approximated by the process

$$\begin{aligned} \tilde{\beta }_{n,3}(t) = \sqrt{n} \left( \hat{\vartheta }-\vartheta _0 \right) \, \tilde{g}_t^{\prime }\left( \vartheta _0\right) , \end{aligned}$$


$$\begin{aligned} \tilde{g}_t^{\prime }(\vartheta _0)&= \frac{\partial \mathcal{{F}}_{\vartheta _0} \left( \mathcal{{F}}_{\vartheta _0}^{-1}(t) \right) }{\partial x} \cdot \left( \frac{\partial \mathcal{{F}}_{\vartheta _0}^{-1}(t)}{\partial t} \cdot \tilde{h}_t^{\prime }(\vartheta _0) + \frac{\partial \mathcal{{F}}_{\vartheta _0}^{-1}(t)}{\partial \vartheta } \right) ,\\ \tilde{h}_t^{\prime }(\vartheta _0)&= \varphi \left( \varPhi ^{-1}(t)\right) \cdot \left( \mu _W+\varPhi ^{-1}(t) \ \rho \ \sigma _W\right) . \end{aligned}$$

Figure 8 in the TR shows the simulated mean and standard deviation of \(\hat{\beta }_{n,3}\) in (7.2), \(\mathring{\beta }_{n,3}\) in (7.3), and \(\tilde{\beta }_{n,3}\) in (7.4) for sample size \(n=40\) and \(n=640\), again for the exponential distribution. The mean functions take on very small values; the standard deviations are very similar in all cases.

Figure 9 of the TR shows the function \(\tilde{h}_t^{\prime }(\vartheta _0)\), the part inside the brackets in \(\tilde{g}_t^{\prime }(\vartheta _0)\), and \(\tilde{g}_t^{\prime }(\vartheta _0)\) itself. The values of \(\tilde{g}_t^{\prime }(\vartheta _0)\) are close to zero on the whole interval. For this reason, \(\hat{\beta }_{n,3}\) is negligible in comparison to \(\hat{\beta }_{n,2}\) for the exponential case at hand.

We also performed Monte Carlo experiments for other gamma distributions with shape parameter not equal to one. These experiments lead to qualitatively similar results and although not reported here they are available from the authors upon request. A reasonable overall conclusion seems to be that under different sampling scenarios the processes \(\hat{\beta }_{n,1}\) and \(\hat{\beta }_{n,3}\) in decomposition (Sect. 7.1) are asymptotically negligible, and hence the behavior of the process \(\hat{\beta }_{n}\) of the Chen–Balakrishnan transformation is dominated by the values of the process \(\hat{\beta }_{n,2}\). The latter process however coincides with the process \(\hat{\alpha }_n(t)\) in Sect. 7.1 of the appendix which is involved in goodness-of-fit testing for normality with estimated parameters, and this fact justifies the validity of the Chen–Balakrishnan transformation.

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Goldmann, C., Klar, B. & Meintanis, S.G. Data transformations and goodness-of-fit tests for type-II right censored samples. Metrika 78, 59–83 (2015).

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  • Empirical characteristic function
  • Empirical distribution function
  • Goodness-of-fit test
  • Censored data