GLR Charts for Simultaneously Monitoring a Sustained Shift and a Linear Drift in the Process Mean

Choi, Mi Lim; Lee, Jae Heon

doi:10.5351/csam.2014.21.1.069

Cited by 4 publications

(1 citation statement)

References 18 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gaussian processes are widely used in many scientific fields (Choi and Lee, 2014;Lee and Park, 2017). A sine-cosine process is the simplest Gaussian wave process written as…”

Section: Introductionmentioning

confidence: 99%

Genetic association tests when a nuisance parameter is not identifiable under no association

Kim

2017

CSAM

View full text Add to dashboard Cite

Some genetic association tests include an unidentifiable nuisance parameter under the null hypothesis of no association. When the mode of inheritance (MOI) is not specified in a case-control design, the Cochran-Armitage (CA) trend test contains an unidentifiable nuisance parameter. The transmission disequilibrium test (TDT) in a family-based association study that includes the unaffected also contains an unidentifiable nuisance parameter. The hypothesis tests that include an unidentifiable nuisance parameter are typically performed by taking a supremum of the CA tests or TDT over reasonable values of the parameter. The p-values of the supremum test statistics cannot be obtained by a normal or chi-square distribution. A common method is to use a Davies's upper bound of the p-value instead of an exact asymptotic p-value. In this paper, we provide a unified sine-cosine process expression of the CA trend test that does not specify the MOI and the TDT that includes the unaffected. We also present a closed form expression of the exact asymptotic formulas to calculate the p-values of the supremum tests when the score function can be written as a linear form in an unidentifiable parameter. We illustrate how to use the derived formulas using a pharmacogenetics case-control dataset and an attention deficit hyperactivity disorder family-based example.

show abstract

“…Gaussian processes are widely used in many scientific fields (Choi and Lee, 2014;Lee and Park, 2017). A sine-cosine process is the simplest Gaussian wave process written as…”

Section: Introductionmentioning

confidence: 99%

Genetic association tests when a nuisance parameter is not identifiable under no association

Kim

2017

CSAM

View full text Add to dashboard Cite

show abstract

A note on GLR charts for monitoring count processes

Lee

Woodall

2018

Quality & Reliability Eng

View full text Add to dashboard Cite

Various generalized likelihood ratio (GLR) charts have been proposed to monitor count processes such as binomial, Bernoulli, Poisson, and multinomial processes. The advantages of GLR charts are that designing the chart is relatively easy, estimates of the process change‐point and shift size are available for post‐signal diagnosis, and they are effective in detecting a wide range of shifts in the process parameter. However, for some special cases of the observations, such as observing all defective items or all non‐defective items, the GLR chart statistic for monitoring a count process has been said to be undefined. We show that the GLR chart statistic is always well defined.

show abstract

A GLR Chart for Monitoring a Zero-Inflated Poisson Process

Choi¹,

Lee²

2014

Korean Journal of Applied Statistics

View full text Add to dashboard Cite

The number of nonconformities in a unit is commonly modeled by a Poisson distribution. As an extension of a Poisson distribution, a zero-inflated Poisson(ZIP) process can be used to fit count data with an excessive number of zeroes. In this paper, we propose a generalized likelihood ratio(GLR) chart to monitor shifts in the two parameters of the ZIP process. We also compare the proposed GLR chart with the combined cumulative sum(CUSUM) chart and the single CUSUM chart. It is shown that the overall performance of the GLR chart is comparable with CUSUM charts and is significantly better in some cases where the actual directions of the shifts are different from the pre-specified directions in CUSUM charts.

show abstract

GLR Charts for Simultaneously Monitoring a Sustained Shift and a Linear Drift in the Process Mean

Cited by 4 publications

References 18 publications

Genetic association tests when a nuisance parameter is not identifiable under no association

Genetic association tests when a nuisance parameter is not identifiable under no association

A note on GLR charts for monitoring count processes

A GLR Chart for Monitoring a Zero-Inflated Poisson Process

Contact Info

Product

Resources

About