Description

Estimate the mean and coefficient of variation of a lognormal distribution given a sample of data that has been subjected to Type I censoring, and optionally construct a confidence interval for the mean.

Usage

Arguments

Details

If x or censored contain any missing (NA), undefined (NaN) or infinite (Inf, -Inf) values, they will be removed prior to performing the estimation.

Let \underline{x} be a vector of n observations from a lognormal distribution with parameters mean=θ and cv=τ. Let η denote the standard deviation of this distribution, so that η = θ τ. Set \underline{y} = log(\underline{x}). Then \underline{y} is a vector of observations from a normal distribution with parameters mean=μ and sd=σ. See the help file for LognormalAlt for the relationship between θ, τ, η, μ, and σ.

Let \underline{x} denote a vector of N observations from a lognormal distribution with parameters mean=θ and cv=τ. Let η denote the standard deviation of this distribution, so that η = θ τ. Set \underline{y} = log(\underline{x}). Then \underline{y} is a vector of observations from a normal distribution with parameters mean=μ and sd=σ. See the help file for LognormalAlt for the relationship between θ, τ, η, μ, and σ.

Assume n (0 < n < N) of the N observations are known and c (c=N-n) of the observations are all censored below (left-censored) or all censored above (right-censored) at k fixed censoring levels

T_1, T_2, …, T_k; \; k ≥ 1 \;\;\;\;\;\; (1)

For the case when k ≥ 2, the data are said to be Type I multiply censored. For the case when k=1, set T = T_1. If the data are left-censored and all n known observations are greater than or equal to T, or if the data are right-censored and all n known observations are less than or equal to T, then the data are said to be Type I singly censored (Nelson, 1982, p.7), otherwise they are considered to be Type I multiply censored.

Let c_j denote the number of observations censored below or above censoring level T_j for j = 1, 2, …, k, so that

∑_{i=1}^k c_j = c \;\;\;\;\;\; (2)

Let x_{(1)}, x_{(2)}, …, x_{(N)} denote the “ordered” observations, where now “observation” means either the actual observation (for uncensored observations) or the censoring level (for censored observations). For right-censored data, if a censored observation has the same value as an uncensored one, the uncensored observation should be placed first. For left-censored data, if a censored observation has the same value as an uncensored one, the censored observation should be placed first.

Note that in this case the quantity x_{(i)} does not necessarily represent the i'th “largest” observation from the (unknown) complete sample.

Finally, let Ω (omega) denote the set of n subscripts in the “ordered” sample that correspond to uncensored observations.

ESTIMATION
This section explains how each of the estimators of mean=θ and cv=τ are computed. The approach is to first compute estimates of θ and η^2 (the mean and variance of the lognormal distribution), say \hat{θ} and \hat{η}^2, then compute the estimate of the cv τ by \hat{τ} = \hat{η}/\hat{θ}.

Maximum Likelihood Estimation (method="mle")
The maximum likelihood estimators of θ, τ, and η are computed as:

\hat{θ}_{mle} = exp(\hat{μ}_{mle} + \frac{\hat{σ}^2_{mle}}{2}) \;\;\;\;\;\; (3)

\hat{τ}_{mle} = [exp(\hat{σ}^2_{mle}) - 1]^{1/2} \;\;\;\;\;\; (4)

\hat{η}_{mle} = \hat{θ}_{mle} \; \hat{τ}_{mle} \;\;\;\;\;\; (5)

where \hat{μ}_{mle} and \hat{σ}_{mle} denote the maximum likelihood estimators of μ and σ. See the help for for enormCensored for information on how \hat{μ}_{mle} and \hat{σ}_{mle} are computed.

Quasi Minimum Variance Unbiased Estimation Based on the MLE's (method="qmvue")
The maximum likelihood estimators of θ and η^2 are biased. Even for complete (uncensored) samples these estimators are biased (see equation (12) in the help file for elnormAlt). The bias tends to 0 as the sample size increases, but it can be considerable for small sample sizes. (Cohn et al., 1989, demonstrate the bias for complete data sets.) For the case of complete samples, the minimum variance unbiased estimators (mvue's) of θ and η^2 were derived by Finney (1941) and are discussed in Gilbert (1987, pp.164-167) and Cohn et al. (1989). These estimators are computed as:

\hat{θ}_{mvue} = e^{\bar{y}} g_{n-1}(\frac{s^2}{2}) \;\;\;\;\;\; (6)

\hat{η}^2_{mvue} = e^{2 \bar{y}} \{g_{n-1}(2s^2) - g_{n-1}[\frac{(n-2)s^2}{n-1}]\} \;\;\;\;\;\; (7)

where

\bar{y} = \frac{1}{n} ∑_{i=1}^n y_i \;\;\;\;\;\; (8)

s^2 = \frac{1}{n-1} ∑_{i=1}^n (y_i - \bar{y})^2 \;\;\;\;\;\; (9)

g_m(z) = ∑_{i=0}^∞ \frac{m^i (m+2i)}{m(m+2) \cdots (m+2i)} (\frac{m}{m+1})^i (\frac{z^i}{i!}) \;\;\;\;\;\; (10)

(see the help file for elnormAlt).

For Type I censored samples, the quasi minimum variance unbiased estimators (qmvue's) of θ and η^2 are computed using equations (6) and (7) and estimating μ and σ with their mle's (see elnormCensored).

For singly censored data, this is apparently the LM method of Gilliom and Helsel (1986, p.137) (it is not clear from their description on page 137 whether their LM method is the straight method="mle" described above or method="qmvue" described here). This method was also used by Newman et al. (1989, p.915, equations 10-11).

For multiply censored data, this is apparently the MM method of Helsel and Cohn (1988, p.1998). (It is not clear from their description on page 1998 and the description in Gilliom and Helsel, 1986, page 137 whether Helsel and Cohn's (1988) MM method is the straight method="mle" described above or method="qmvue" described here.)

Bias-Corrected Maximum Likelihood Estimation (method="bcmle")
This method was derived by El-Shaarawi (1989) and can be applied to complete or censored data sets. For complete data, the exact relative bias of the mle of the mean θ is given as:

B_{mle} = \frac{E[\hat{θ}_{mle}]}{θ} = exp[\frac{-(n-1)σ^2}{2n}] (1 - \frac{σ^2}{n})^{-(n-1)/2} \;\;\;\;\;\; (11)

(see equation (12) in the help file for elnormAlt).

For the case of complete or censored data, El-Shaarawi (1989) proposed the following “bias-corrected” maximum likelihood estimator:

\hat{θ}_{bcmle} = \frac{\hat{θ}_{mle}}{\hat{B}_{mle}} \;\;\;\;\;\; (12)

where

\hat{B}_{mle} = exp[\frac{1}{2}(\hat{V}_{11} + 2\hat{σ}_{mle} \hat{V}_{12} + \hat{σ}^2_{mle} \hat{V}_{22})] \;\;\;\;\;\; (13)

and V denotes the asymptotic variance-covariance of the mle's of μ and σ, which is based on the observed information matrix, formulas for which are given in Cohen (1991). El-Shaarawi (1989) does not propose a bias-corrected estimator of the variance η^2, so the mle of η is computed when method="bcmle".

Robust Regression on Order Statistics (method="rROS") or
Imputation Using Quantile-Quantile Regression (method ="impute.w.qq.reg")
This is the robust Regression on Order Statistics (rROS) method discussed in USEPA (2009) and Helsel (2012). This method involves using quantile-quantile regression on the log-transformed observations to fit a regression line (and thus initially estimate the mean μ and standard deviation σ in log-space), imputing the log-transformed values of the c censored observations by predicting them from the regression equation, transforming the log-scale imputed values back to the original scale, and then computing the method of moments estimates of the mean and standard deviation based on the observed and imputed values.

The steps are:

1. Estimate μ and σ by computing the least-squares estimates in the following model:

   y_{(i)} = μ + σ Φ^{-1}(p_i) + ε_i, \; i \in Ω \;\;\;\;\;\; (14)

   where p_i denotes the plotting position associated with the i'th largest value, a is a constant such that 0 ≤ a ≤ 1 (the default value is 0.375), Φ denotes the cumulative distribution function (cdf) of the standard normal distribution and Ω denotes the set of n subscripts associated with the uncensored observations in the ordered sample. The plotting positions are computed by calling the function ppointsCensored.
   

2. Compute the log-scale imputed values as:

   \hat{y}_{(i)} = \hat{μ}_{qqreg} + \hat{σ}_{qqreg} Φ^{-1}(p_i), \; i \not \in Ω \;\;\;\;\;\; (15)

   
   

3. Retransform the log-scale imputed values:

   \hat{x}_{(i)} = exp[\hat{y}_{(i)}], \; i \not \in Ω \;\;\;\;\;\; (16)

   
   

4. Compute the usual method of moments estimates of the mean and variance.

   \hat{θ} = \frac{1}{N} [∑_{i \not \in Ω} \hat{x}_{(i)} + ∑_{i \in Ω} x_{(i)}] \;\;\;\;\;\; (17)

   \hat{η}^2 = \frac{1}{N-1} [∑_{i \not \in Ω} (\hat{x}_{(i)} - \hat{θ}^2) + ∑_{i \in Ω} (x_{(i)} - \hat{θ}^2)] \;\;\;\;\;\; (18)

   Note that the estimate of variance is actually the usual unbiased one (not the method of moments one) in the case of complete data.

For sinlgy censored data, this method is discussed by Hashimoto and Trussell (1983), Gilliom and Helsel (1986), and El-Shaarawi (1989), and is referred to as the LR (Log-Regression) or Log-Probability Method.

For multiply censored data, this is the MR method of Helsel and Cohn (1988, p.1998). They used it with the probability method of Hirsch and Stedinger (1987) and Weibull plotting positions (i.e., prob.method="hirsch-stedinger" and plot.pos.con=0).

The argument plot.pos.con (see the entry for ... in the ARGUMENTS section above) determines the value of the plotting positions computed in equations (14) and (15) when method equals "hirsch-stedinger" or "michael-schucany". The default value is plot.pos.con=0.375. See the help file for ppointsCensored for more information.

The arguments lb.impute and ub.impute (see the entry for ... in the ARGUMENTS section above) determine the lower and upper bounds for the imputed values. Imputed values smaller than lb.impute are set to this value. Imputed values larger than ub.impute are set to this value. The default values are lb.impute=0 and ub.impute=Inf.

Imputation Using Quantile-Quantile Regression Including the Censoring Level
(method ="impute.w.qq.reg.w.cen.level")
This method is only available for sinlgy censored data. This method was proposed by El-Shaarawi (1989), which he denoted as the Modified LR Method. It is exactly the same method as imputation using quantile-quantile regression (method="impute.w.qq.reg"), except that the quantile-quantile regression includes the censoring level. For left singly censored data, the modification involves adding the point [Φ^{-1}(p_c), T] to the plot before fitting the least-squares line. For right singly censored data, the point [Φ^{-1}(p_{n+1}), T] is added to the plot before fitting the least-squares line.

Imputation Using Maximum Likelihood (method ="impute.w.mle")
This method is only available for sinlgy censored data. This is exactly the same method as robust Regression on Order Statistics (i.e., the same as using method="rROS" or
method="impute.w.qq.reg"), except that the maximum likelihood method (method="mle") is used to compute the initial estimates of the mean and standard deviation. In the context of lognormal data, this method is discussed by El-Shaarawi (1989), which he denotes as the Modified Maximum Likelihood Method.

Setting Censored Observations to Half the Censoring Level (method="half.cen.level")
This method is applicable only to left censored data that is bounded below by 0. This method involves simply replacing all the censored observations with half their detection limit, and then computing the usual moment estimators of the mean and variance. That is, all censored observations are imputed to be half the detection limit, and then Equations (17) and (18) are used to estimate the mean and varaince.

This method is included only to allow comparison of this method to other methods. Setting left-censored observations to half the censoring level is not recommended. In particular, El-Shaarawi and Esterby (1992) show that these estimators are biased and inconsistent (i.e., the bias remains even as the sample size increases).

CONFIDENCE INTERVALS
This section explains how confidence intervals for the mean θ are computed. f

Likelihood Profile (ci.method="profile.likelihood")
This method was proposed by Cox (1970, p.88), and Venzon and Moolgavkar (1988) introduced an efficient method of computation. This method is also discussed by Stryhn and Christensen (2003) and Royston (2007). The idea behind this method is to invert the likelihood-ratio test to obtain a confidence interval for the mean θ while treating the coefficient of variation τ as a nuisance parameter.

For Type I left censored data, the likelihood function is given by:

L(θ, τ | \underline{x}) = {N \choose c_1 c_2 … c_k n} ∏_{j=1}^k [F(T_j)]^{c_j} ∏_{i \in Ω} f[x_{(i)}] \;\;\;\;\;\; (19)

where f and F denote the probability density function (pdf) and cumulative distribution function (cdf) of the population. That is,

f(t) = φ(\frac{t-μ}{σ}) \;\;\;\;\;\; (20)

F(t) = Φ(\frac{t-μ}{σ}) \;\;\;\;\;\; (21)

where

μ = log(\frac{θ}{√{τ^2 + 1}}) \;\;\;\;\;\; (22)

σ = [log(τ^2 + 1)]^{1/2} \;\;\;\;\;\; (23)

and φ and Φ denote the pdf and cdf of the standard normal distribution, respectively (Cohen, 1963; 1991, pp.6, 50). For left singly censored data, equation (3) simplifies to:

L(μ, σ | \underline{x}) = {N \choose c} [F(T)]^{c} ∏_{i = c+1}^n f[x_{(i)}] \;\;\;\;\;\; (24)

Similarly, for Type I right censored data, the likelihood function is given by:

L(μ, σ | \underline{x}) = {N \choose c_1 c_2 … c_k n} ∏_{j=1}^k [1 - F(T_j)]^{c_j} ∏_{i \in Ω} f[x_{(i)}] \;\;\;\;\;\; (25)

and for right singly censored data this simplifies to:

L(μ, σ | \underline{x}) = {N \choose c} [1 - F(T)]^{c} ∏_{i = 1}^n f[x_{(i)}] \;\;\;\;\;\; (26)

Following Stryhn and Christensen (2003), denote the maximum likelihood estimates of the mean and coefficient of variation by (θ^*, τ^*). The likelihood ratio test statistic (G^2) of the hypothesis H_0: θ = θ_0 (where θ_0 is a fixed value) equals the drop in 2 log(L) between the “full” model and the reduced model with θ fixed at θ_0, i.e.,

G^2 = 2 \{log[L(θ^*, τ^*)] - log[L(θ_0, τ_0^*)]\} \;\;\;\;\;\; (27)

where τ_0^* is the maximum likelihood estimate of τ for the reduced model (i.e., when θ = θ_0). Under the null hypothesis, the test statistic G^2 follows a chi-squared distribution with 1 degree of freedom.

Alternatively, we may express the test statistic in terms of the profile likelihood function L_1 for the mean θ, which is obtained from the usual likelihood function by maximizing over the parameter τ, i.e.,

L_1(θ) = max_{τ} L(θ, τ) \;\;\;\;\;\; (28)

Then we have

G^2 = 2 \{log[L_1(θ^*)] - log[L_1(θ_0)]\} \;\;\;\;\;\; (29)

A two-sided (1-α)100\% confidence interval for the mean θ consists of all values of θ_0 for which the test is not significant at level alpha:

θ_0: G^2 ≤ χ^2_{1, {1-α}} \;\;\;\;\;\; (30)

where χ^2_{ν, p} denotes the p'th quantile of the chi-squared distribution with ν degrees of freedom. One-sided lower and one-sided upper confidence intervals are computed in a similar fashion, except that the quantity 1-α in Equation (30) is replaced with 1-2α.

Direct Normal Approximations (ci.method="delta" or ci.method="normal.approx")
An approximate (1-α)100\% confidence interval for θ can be constructed assuming the distribution of the estimator of θ is approximately normally distributed. That is, a two-sided (1-α)100\% confidence interval for θ is constructed as:

[\hat{θ} - t_{1-α/2, m-1}\hat{σ}_{\hat{θ}}, \; \hat{θ} + t_{1-α/2, m-1}\hat{σ}_{\hat{θ}}] \;\;\;\;\;\; (31)

where \hat{θ} denotes the estimate of θ, \hat{σ}_{\hat{θ}} denotes the estimated asymptotic standard deviation of the estimator of θ, m denotes the assumed sample size for the confidence interval, and t_{p,ν} denotes the p'th quantile of Student's t-distribuiton with ν degrees of freedom. One-sided confidence intervals are computed in a similar fashion.

The argument ci.sample.size determines the value of m (see see the entry for ... in the ARGUMENTS section above). When method equals "mle", "qmvue", or "bcmle" and the data are singly censored, the default value is the expected number of uncensored observations, otherwise it is n, the observed number of uncensored observations. This is simply an ad-hoc method of constructing confidence intervals and is not based on any published theoretical results.

When pivot.statistic="z", the p'th quantile from the standard normal distribution is used in place of the p'th quantile from Student's t-distribution.

Direct Normal Approximation Based on the Delta Method (ci.method="delta")
This method is usually applied with the maximum likelihood estimators (method="mle"). It should also work approximately for the quasi minimum variance unbiased estimators (method="qmvue") and the bias-corrected maximum likelihood estimators (method="bcmle").

When method="mle", the variance of the mle of θ can be estimated based on the variance-covariance matrix of the mle's of μ and σ (denoted V), and the delta method:

\hat{σ}^2_{\hat{θ}} = (\frac{\partial θ}{\partial \underline{λ}})^{'}_{\hat{\underline{λ}}} \hat{V} (\frac{\partial θ}{\partial \underline{λ}})_{\hat{\underline{λ}}} \;\;\;\;\;\; (32)

where

\underline{λ}' = (μ, σ) \;\;\;\;\;\; (33)

\frac{\partial θ}{\partial μ} = exp(μ + \frac{σ^2}{2}) \;\;\;\;\;\; (34)

\frac{\partial θ}{\partial σ} = σ exp(μ + \frac{σ^2}{2}) \;\;\;\;\;\; (35)

(Shumway et al., 1989). The variance-covariance matrix V of the mle's of μ and σ is estimated based on the inverse of the observed Fisher Information matrix, formulas for which are given in Cohen (1991).

Direct Normal Approximation Based on the Moment Estimators (ci.method="normal.approx")
This method is valid only for the moment estimators based on imputed values
(i.e., method="impute.w.qq.reg" or method="half.cen.level"). For these cases, the standard deviation of the estimated mean is assumed to be approximated by

\hat{σ}_{\hat{θ}} = \frac{\hat{η}}{√{m}} \;\;\;\;\;\; (36)

where, as already noted, m denotes the assumed sample size. This is simply an ad-hoc method of constructing confidence intervals and is not based on any published theoretical results.

Cox's Method (ci.method="cox")
This method may be applied with the maximum likelihood estimators (method="mle"), the quasi minimum variance unbiased estimators (method="qmvue"), and the bias-corrected maximum likelihood estimators (method="bcmle").

This method was proposed by El-Shaarawi (1989) and is an extension of the method derived by Cox and presented in Land (1972) for the case of complete data (see the explanation of ci.method="cox" in the help file for elnormAlt). The idea is to construct an approximate (1-α)100\% confidence interval for the quantity

β = exp(μ + \frac{σ^2}{2}) \;\;\;\;\;\; (37)

assuming the estimate of β

\hat{β} = exp(\hat{μ} + \frac{\hat{σ}^2}{2}) \;\;\;\;\;\; (38)

is approximately normally distributed, and then exponentiate the confidence limits. That is, a two-sided (1-α)100\% confidence interval for θ is constructed as:

[ exp(\hat{β} - h), \; exp(\hat{β} + h) ]\;\;\;\;\;\; (39)

where

h = t_{1-α/2, m-1}\hat{σ}_{\hat{β}} \;\;\;\;\;\; (40)

and \hat{σ}_{\hat{β}} denotes the estimated asymptotic standard deviation of the estimator of β, m denotes the assumed sample size for the confidence interval, and t_{p,ν} denotes the p'th quantile of Student's t-distribuiton with ν degrees of freedom.

El-Shaarawi (1989) shows that the standard deviation of the mle of β can be estimated by:

\hat{σ}_{\hat{β}} = √{ \hat{V}_{11} + 2 \hat{σ} \hat{V}_{12} + \hat{σ}^2 \hat{V}_{22} } \;\;\;\;\;\; (41)

where V denotes the variance-covariance matrix of the mle's of μ and σ and is estimated based on the inverse of the Fisher Information matrix.

One-sided confidence intervals are computed in a similar fashion.

Bootstrap and Bias-Corrected Bootstrap Approximation (ci.method="bootstrap")
The bootstrap is a nonparametric method of estimating the distribution (and associated distribution parameters and quantiles) of a sample statistic, regardless of the distribution of the population from which the sample was drawn. The bootstrap was introduced by Efron (1979) and a general reference is Efron and Tibshirani (1993).

In the context of deriving an approximate (1-α)100\% confidence interval for the population mean θ, the bootstrap can be broken down into the following steps:

1. Create a bootstrap sample by taking a random sample of size N from the observations in \underline{x}, where sampling is done with replacement. Note that because sampling is done with replacement, the same element of \underline{x} can appear more than once in the bootstrap sample. Thus, the bootstrap sample will usually not look exactly like the original sample (e.g., the number of censored observations in the bootstrap sample will often differ from the number of censored observations in the original sample).

2. Estimate θ based on the bootstrap sample created in Step 1, using the same method that was used to estimate θ using the original observations in \underline{x}. Because the bootstrap sample usually does not match the original sample, the estimate of θ based on the bootstrap sample will usually differ from the original estimate based on \underline{x}.

3. Repeat Steps 1 and 2 B times, where B is some large number. The number of bootstraps B is determined by the argument n.bootstraps (see the section ARGUMENTS above). The default value of n.bootstraps is 1000.

4. Use the B estimated values of θ to compute the empirical cumulative distribution function of this estimator of θ (see ecdfPlot), and then create a confidence interval for θ based on this estimated cdf.

The two-sided percentile interval (Efron and Tibshirani, 1993, p.170) is computed as:

[\hat{G}^{-1}(\frac{α}{2}), \; \hat{G}^{-1}(1-\frac{α}{2})] \;\;\;\;\;\; (42)

where \hat{G}(t) denotes the empirical cdf evaluated at t and thus \hat{G}^{-1}(p) denotes the p'th empirical quantile, that is, the p'th quantile associated with the empirical cdf. Similarly, a one-sided lower confidence interval is computed as:

[\hat{G}^{-1}(α), \; ∞] \;\;\;\;\;\; (43)

and a one-sided upper confidence interval is computed as:

[0, \; \hat{G}^{-1}(1-α)] \;\;\;\;\;\; (44)

The function elnormAltCensored calls the R function quantile to compute the empirical quantiles used in Equations (42)-(44).

The percentile method bootstrap confidence interval is only first-order accurate (Efron and Tibshirani, 1993, pp.187-188), meaning that the probability that the confidence interval will contain the true value of θ can be off by k/√{N}, where kis some constant. Efron and Tibshirani (1993, pp.184-188) proposed a bias-corrected and accelerated interval that is second-order accurate, meaning that the probability that the confidence interval will contain the true value of θ may be off by k/N instead of k/√{N}. The two-sided bias-corrected and accelerated confidence interval is computed as:

[\hat{G}^{-1}(α_1), \; \hat{G}^{-1}(α_2)] \;\;\;\;\;\; (45)

where

α_1 = Φ[\hat{z}_0 + \frac{\hat{z}_0 + z_{α/2}}{1 - \hat{a}(z_0 + z_{α/2})}] \;\;\;\;\;\; (46)

α_2 = Φ[\hat{z}_0 + \frac{\hat{z}_0 + z_{1-α/2}}{1 - \hat{a}(z_0 + z_{1-α/2})}] \;\;\;\;\;\; (47)

\hat{z}_0 = Φ^{-1}[\hat{G}(\hat{θ})] \;\;\;\;\;\; (48)

\hat{a} = \frac{∑_{i=1}^N (\hat{θ}_{(\cdot)} - \hat{θ}_{(i)})^3}{6[∑_{i=1}^N (\hat{θ}_{(\cdot)} - \hat{θ}_{(i)})^2]^{3/2}} \;\;\;\;\;\; (49)

where the quantity \hat{θ}_{(i)} denotes the estimate of θ using all the values in \underline{x} except the i'th one, and

\hat{θ}{(\cdot)} = \frac{1}{N} ∑_{i=1}^N \hat{θ_{(i)}} \;\;\;\;\;\; (50)

A one-sided lower confidence interval is given by:

[\hat{G}^{-1}(α_1), \; ∞] \;\;\;\;\;\; (51)

and a one-sided upper confidence interval is given by:

[0, \; \hat{G}^{-1}(α_2)] \;\;\;\;\;\; (52)

where α_1 and α_2 are computed as for a two-sided confidence interval, except α/2 is replaced with α in Equations (51) and (52).

The constant \hat{z}_0 incorporates the bias correction, and the constant \hat{a} is the acceleration constant. The term “acceleration” refers to the rate of change of the standard error of the estimate of θ with respect to the true value of θ (Efron and Tibshirani, 1993, p.186). For a normal (Gaussian) distribution, the standard error of the estimate of θ does not depend on the value of θ, hence the acceleration constant is not really necessary.

When ci.method="bootstrap", the function elnormAltCensored computes both the percentile method and bias-corrected and accelerated method bootstrap confidence intervals.

This method of constructing confidence intervals for censored data was studied by Shumway et al. (1989).

Value

a list of class "estimateCensored" containing the estimated parameters and other information. See estimateCensored.object for details.

Note

A sample of data contains censored observations if some of the observations are reported only as being below or above some censoring level. In environmental data analysis, Type I left-censored data sets are common, with values being reported as “less than the detection limit” (e.g., Helsel, 2012). Data sets with only one censoring level are called singly censored; data sets with multiple censoring levels are called multiply or progressively censored.

Statistical methods for dealing with censored data sets have a long history in the field of survival analysis and life testing. More recently, researchers in the environmental field have proposed alternative methods of computing estimates and confidence intervals in addition to the classical ones such as maximum likelihood estimation.

Helsel (2012, Chapter 6) gives an excellent review of past studies of the properties of various estimators based on censored environmental data.

In practice, it is better to use a confidence interval for the mean or a joint confidence region for the mean and standard deviation, rather than rely on a single point-estimate of the mean. Since confidence intervals and regions depend on the properties of the estimators for both the mean and standard deviation, the results of studies that simply evaluated the performance of the mean and standard deviation separately cannot be readily extrapolated to predict the performance of various methods of constructing confidence intervals and regions. Furthermore, for several of the methods that have been proposed to estimate the mean based on type I left-censored data, standard errors of the estimates are not available, hence it is not possible to construct confidence intervals (El-Shaarawi and Dolan, 1989).

Few studies have been done to evaluate the performance of methods for constructing confidence intervals for the mean or joint confidence regions for the mean and standard deviation on the original scale, not the log-scale, when data are subjected to single or multiple censoring. See, for example, Singh et al. (2006).

Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

References

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Cohen, A.C. (1959). Simplified Estimators for the Normal Distribution When Samples are Singly Censored or Truncated. Technometrics 1(3), 217–237.

Cohen, A.C. (1963). Progressively Censored Samples in Life Testing. Technometrics 5, 327–339

Cohen, A.C. (1991). Truncated and Censored Samples. Marcel Dekker, New York, New York, 312pp.

Cox, D.R. (1970). Analysis of Binary Data. Chapman & Hall, London. 142pp.

Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics 7, 1–26.

Efron, B., and R.J. Tibshirani. (1993). An Introduction to the Bootstrap. Chapman and Hall, New York, 436pp.

El-Shaarawi, A.H. (1989). Inferences About the Mean from Censored Water Quality Data. Water Resources Research 25(4) 685–690.

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See Also

LognormalAlt, elnormAlt, elnormCensored, enormCensored, estimateCensored.object.

Examples