Coverage probability (%) – the percentage of simulated recruitment falling inside the 95% prediction intervals of the curvilinear regression (Eq. 7) – for each amplitude of recruitment variability (Rvar, %; see section 2.2.3).

Relative mean bias percentage, RMSE and coverage probability when N = 30000, m = 30, β1 = 0.1, β2 = 0.2, ρb = 0.6, ρw = 0.3.

Binod Neupane (679579)
Joseph Beyene (62170)

July 2015

Relative mean bias percentage, RMSE and coverage probability when N = 30000, m = 30, β<sub...

Changes in coverage probabilities of 95% confidence intervals for population estimates.

Sandra L. Taylor (169294)
Katherine S. Pollard (14217)

February 2013

The figures show the changes in coverage probability relative to detection probability for the lo...

The median, lower and upper 5 percentiles of the coverage probabilities of the tolerance intervals, TI0 (dark gray area) and TI1 (light gray area).

Hungyen Chen (662956)
Hirohisa Kishino (25350)

October 2015

For each area, middle, lower, and upper curves represent the median, lower and upper 5 percentile...

Coverage probability (in percentage, %) of the estimated overall effect size’s 95% confidence interval in the simulation studies.

Lifeng Lin (2034385)

September 2018

Coverage probability (in percentage, %) of the estimated overall effect size’s 95% confidence interv...

Coverage probabilities for those intervals that do not include zero (OR , for the prediction interval approach (solid lines: independency; broken lines: moderate correlation; dotted lines: strong correlation) and for the confidence interval approach (constant solid lines), when logORs are normally distributed.

Wan-Yu Lin (91498)
Wen-Chung Lee (73587)

February 2012

Coverage probabilities for those intervals that do not include zero (OR , for the prediction inte...

The simulated coverage probabilities and errors for the 90% two-sided reference intervals of the distribution proportion 0.80.

Gwowen Shieh (3106215)

November 2022

The simulated coverage probabilities and errors for the 90% two-sided reference intervals of the dis...

Parameter values for the relationship between recruitment and the minimum F40 (Eq. 7) under a simulated recruitment variability (Rvar) of 100%.

Stéphane Thanassekos (668662)
Martin J. Cox (409559)
Keith Reid (668663)

December 2014

Parameters for the 95% prediction interval are included (upper and lower). The parameter variance...

Prediction error (%, Eq. 4) computed between the simulated recruitment and the recruitment estimated with the multiannual formula using past values of minimum F40 (Eq. 8), as a function of the number of values of minimum F40 included in the regression (from 1 to 6), for three amplitudes of recruitment variability (A–C; Rvar, %; see section 2.2.3).

Stéphane Thanassekos (668662)
Martin J. Cox (409559)
Keith Reid (668663)

December 2014

In this boxplot, the range of each box corresponds to the interquartile range (IQR) and the whisk...

Coverage probabilities and widths of 95% confidence intervals for the optimal cutpoints on lognormally distributed data.

Christian Thiele (12519480)
Gerrit Hirschfeld (3586124)

January 2023

Coverage probabilities and widths of 95% confidence intervals for the optimal cutpoints on lognormal...

The error between simulated coverage probability and nominal coverage probability of the 95% two-sided and 97.5% one-sided confidence intervals for the number of groups G = 3, one covariate P = 1, contrast coefficients {–1, 1/2, 1/2}, error variance σ2 = 3.2728, and balanced designs with NT = 15, 30, and 60.

Gwowen Shieh (3106215)

February 2023

The error between simulated coverage probability and nominal coverage probability of the 95% two-sid...

The error between simulated coverage probability and nominal coverage probability of the 90% two-sided and 95% one-sided confidence intervals for the number of groups G = 3, one covariate P = 1, contrast coefficients {–1, 1/2, 1/2}, error variance σ2 = 3.2728, and balanced designs with NT = 15, 30, and 60.

Gwowen Shieh (3106215)

February 2023

The error between simulated coverage probability and nominal coverage probability of the 90% two-sid...

Coverage of the 95% confidence interval estimation method of Bucher for .

Edward J. Mills (34548)
Isabella Ghement (357196)
Christopher O'Regan (357197)
Kristian Thorlund (149383)

February 2013

For each simulation setting, coverage was assessed by tracking the percentage of simulations prod...

Relative mean bias percentage, RMSE and coverage probability when N = 10000, m = 10, β1 = 0.1, β2 = 0.1, ρb = 0.5, ρw = 0.3.

Binod Neupane (679579)
Joseph Beyene (62170)

July 2015

Relative mean bias percentage, RMSE and coverage probability when N = 10000, m = 10, β<sub...

Relative mean bias percentage, RMSE and coverage probability when N = 20000, m = 15, β1 = 0.1, β2 = 0.1, ρb = 0.6, ρw = 0.3.

Binod Neupane (679579)
Joseph Beyene (62170)

July 2015

Relative mean bias percentage, RMSE and coverage probability when N = 20000, m = 15, β<sub...

Coverage probabilities when different numbers of logORs () are considered for the prediction interval approach, assuming logORs are normally distributed.

Wan-Yu Lin (91498)
Wen-Chung Lee (73587)

February 2012

The different panels in the figure are arranged such that the is 1, 0.5, and 0.25 (from top to b...

Relative mean bias percentage, RMSE and coverage probability when N = 30000, m = 30, β1 = 0.1, β2 = 0.2, ρb = 0.6, ρw = 0.3.

Binod Neupane (679579)
Joseph Beyene (62170)

July 2015

Relative mean bias percentage, RMSE and coverage probability when N = 30000, m = 30, β<sub...

Changes in coverage probabilities of 95% confidence intervals for population estimates.

Sandra L. Taylor (169294)
Katherine S. Pollard (14217)

February 2013

The figures show the changes in coverage probability relative to detection probability for the lo...

The median, lower and upper 5 percentiles of the coverage probabilities of the tolerance intervals, TI0 (dark gray area) and TI1 (light gray area).

Hungyen Chen (662956)
Hirohisa Kishino (25350)

October 2015

For each area, middle, lower, and upper curves represent the median, lower and upper 5 percentile...

Coverage probability (in percentage, %) of the estimated overall effect size’s 95% confidence interval in the simulation studies.

Lifeng Lin (2034385)

September 2018

Coverage probability (in percentage, %) of the estimated overall effect size’s 95% confidence interv...

Coverage probabilities for those intervals that do not include zero (OR , for the prediction interval approach (solid lines: independency; broken lines: moderate correlation; dotted lines: strong correlation) and for the confidence interval approach (constant solid lines), when logORs are normally distributed.

Wan-Yu Lin (91498)
Wen-Chung Lee (73587)

February 2012

Coverage probabilities for those intervals that do not include zero (OR , for the prediction inte...

The simulated coverage probabilities and errors for the 90% two-sided reference intervals of the distribution proportion 0.80.

Gwowen Shieh (3106215)

November 2022

The simulated coverage probabilities and errors for the 90% two-sided reference intervals of the dis...

Parameter values for the relationship between recruitment and the minimum F40 (Eq. 7) under a simulated recruitment variability (Rvar) of 100%.

Stéphane Thanassekos (668662)
Martin J. Cox (409559)
Keith Reid (668663)

December 2014

Parameters for the 95% prediction interval are included (upper and lower). The parameter variance...

Prediction error (%, Eq. 4) computed between the simulated recruitment and the recruitment estimated with the multiannual formula using past values of minimum F40 (Eq. 8), as a function of the number of values of minimum F40 included in the regression (from 1 to 6), for three amplitudes of recruitment variability (A–C; Rvar, %; see section 2.2.3).

Stéphane Thanassekos (668662)
Martin J. Cox (409559)
Keith Reid (668663)

December 2014

In this boxplot, the range of each box corresponds to the interquartile range (IQR) and the whisk...

Coverage probabilities and widths of 95% confidence intervals for the optimal cutpoints on lognormally distributed data.

Christian Thiele (12519480)
Gerrit Hirschfeld (3586124)

January 2023

Coverage probabilities and widths of 95% confidence intervals for the optimal cutpoints on lognormal...

The error between simulated coverage probability and nominal coverage probability of the 95% two-sided and 97.5% one-sided confidence intervals for the number of groups G = 3, one covariate P = 1, contrast coefficients {–1, 1/2, 1/2}, error variance σ2 = 3.2728, and balanced designs with NT = 15, 30, and 60.

Gwowen Shieh (3106215)

February 2023

The error between simulated coverage probability and nominal coverage probability of the 95% two-sid...

The error between simulated coverage probability and nominal coverage probability of the 90% two-sided and 95% one-sided confidence intervals for the number of groups G = 3, one covariate P = 1, contrast coefficients {–1, 1/2, 1/2}, error variance σ2 = 3.2728, and balanced designs with NT = 15, 30, and 60.

Gwowen Shieh (3106215)

February 2023

The error between simulated coverage probability and nominal coverage probability of the 90% two-sid...

Coverage of the 95% confidence interval estimation method of Bucher for .

Edward J. Mills (34548)
Isabella Ghement (357196)
Christopher O'Regan (357197)
Kristian Thorlund (149383)

February 2013

For each simulation setting, coverage was assessed by tracking the percentage of simulations prod...

Relative mean bias percentage, RMSE and coverage probability when N = 10000, m = 10, β1 = 0.1, β2 = 0.1, ρb = 0.5, ρw = 0.3.

Binod Neupane (679579)
Joseph Beyene (62170)

July 2015

Relative mean bias percentage, RMSE and coverage probability when N = 10000, m = 10, β<sub...

Relative mean bias percentage, RMSE and coverage probability when N = 20000, m = 15, β1 = 0.1, β2 = 0.1, ρb = 0.6, ρw = 0.3.

Binod Neupane (679579)
Joseph Beyene (62170)

July 2015

Relative mean bias percentage, RMSE and coverage probability when N = 20000, m = 15, β<sub...

Coverage probabilities when different numbers of logORs () are considered for the prediction interval approach, assuming logORs are normally distributed.

Wan-Yu Lin (91498)
Wen-Chung Lee (73587)

February 2012

The different panels in the figure are arranged such that the is 1, 0.5, and 0.25 (from top to b...

Relative mean bias percentage, RMSE and coverage probability when N = 30000, m = 30, β1 = 0.1, β2 = 0.2, ρb = 0.6, ρw = 0.3.

Binod Neupane (679579)
Joseph Beyene (62170)

July 2015

Relative mean bias percentage, RMSE and coverage probability when N = 30000, m = 30, β<sub...

Changes in coverage probabilities of 95% confidence intervals for population estimates.

Sandra L. Taylor (169294)
Katherine S. Pollard (14217)

February 2013

The figures show the changes in coverage probability relative to detection probability for the lo...

The median, lower and upper 5 percentiles of the coverage probabilities of the tolerance intervals, TI0 (dark gray area) and TI1 (light gray area).

Hungyen Chen (662956)
Hirohisa Kishino (25350)

October 2015

For each area, middle, lower, and upper curves represent the median, lower and upper 5 percentile...

Coverage probability (%) – the percentage of simulated recruitment falling inside the 95% prediction intervals of the curvilinear regression (Eq. 7) – for each amplitude of recruitment variability (Rvar, %; see section 2.2.3).

Abstract

Extracted data

Coverage probability (%) – the percentage of simulated recruitment falling inside the 95% prediction intervals of the curvilinear regression (Eq. 7) – for each amplitude of recruitment variability (Rvar, %; see section 2.2.3).

Abstract

Extracted data

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