Inferences on the means of lognormal distributions using generalized p-values and generalized confidence intervals

By Kalimuthu Krishnamoorthy, Thomas Mathew. 2003

216 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ab67a80a170269fd93dbd6f5c7e11da57c10301b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Simple confidence intervals for lognormal means and their differences with environmental applications (2009, methodology): “Section 2 presents the new procedure, after summarizing the GCI (Krishnamoorthy and Mathew, 2003) and the modified Cox method (Armstrong, 1992; ElShaarawi and Lin, 2007).”
Fiducial Generalized Confidence Intervals (2006, background): “Krishnamoorthy and Mathew (2003) have discussed generalized inference for a single lognormal mean, and Krishnamoorthy (unpublished work) has considered generalized inference for the difference between two lognormal means.”
Generalized confidence interval estimation for the mean of delta-lognormal distribution: an application to New Zealand trawl survey data (2014, methodology): “Because we use the normal approximation to the binomial distribution, the GPQ for p is an asymptotic GPQ. Krishnamoorthy and Mathew [11] have constructed a GCI for the mean of lognormal distribution.”
Inference on Reliability in Two-parameter Exponential Stress–strength Model (2007, background): “If µ1 = µ2, then the form for R, as shown in (2), is quite complex, and only large sample approach is available (see Kotz et al. 2003, Section 4.2.3).”
CONFIDENCE LIMITS FOR STRESS STRENGTH RELIABILITY INVOLVING WEIBULL MODELS (2010, methodology): “…has been used successfully to address several complex problems such as estimating lognormal mean and for constructing tolerance limits in one-way random model and some mixed models; see Krishnamoorthy and Mathew (2003, 2004), Liao et al. (2005), and Chapters 4–6 of Krishnamoorthy and Mathew (2009).”
Inference for functions of parameters in discrete distributions based on fiducial approach: Binomial and Poisson cases (2010, background): “We again note that these inconsistencies do not pose serious problems in developing approximate or exact inferential procedures for some real-valued functions of µ and σ2 such as normal quantile or log-normal mean (Krishnamoorthy and Mathew, 2003).”
Confidence Intervals for the Mean of a Log-Normal Distribution (2005, methodology): “The methods include a naïve method based on transformation and backtransformation; a method proposed by Cox; a modified version of the Cox method; a method motivated by large-sample theory; and a method based on generalized confidence intervals (Weerahandi, 1993; Krishnamoorthy and Mathew, 2003).”
Bayesian confidence intervals for means and variances of lognormal and bivariate lognormal distributions (2012, methodology): “These methods have generated a series of papers on the means of lognormal data (see for example Krishnamoorthy and Mathew (2003), Tian (2005), Chen and Zhou (2006), Krishnamoorthy, Mathew and Romachandren (2006), Tian and Wu (2007 [a],[b]) and Bebu and Mathew (2008)).”
Inferences on correlation coefficients : One-sample, independent and correlated cases (2007, unknown): “This GV approach has been used successfully to develop tests and confidence intervals involving “non-standard” parameters, such as lognormal mean (Krishnamoorthy and Mathew, 2003) and normal quantiles (Weerahandi, 1995a).”
A new generalized p-value approach for testing equality of coefficients of variation in k normal populations (2011, background): “See for instance, Zhou and Mathew [13], Weerahandi [14], Krishnamoorthy and Mathew [15], Tian [16], Hu et al. [17], etc. Hu et al. [17] present a new method to construct a generalized p-value for testing the equality of means under classical one-way layout ANOVA model with unbalanced and heterosceda”
Confidence limits and prediction limits for a Weibull distribution based on the generalized variable approach (2009, methodology): “aKMM—approximate limits by Krishnamoorthy et al. (2007) approach.”