…d (subject to g > 0) gives the analytic solution
ĝ = max {
1 d 1 c (β̂ML − ν) >(X>WX)(β̂ML − ν)− 1, 0 }
≈ max{zobs/d− 1, 0}, (5)
here zobs denotes the observed deviance (relative to the null model with β = 0), obtained from fitting a standard GLM to the data at hand (Copas, 1983, 1997).
Sample text:```The GITT model performs better than an empirical lag-time model [17], however, its predicted profiles tend to underestimate Cmax, which, if consistent, may suggest confounding systematic bias or invalid assumptions.```
Sample intent: background
Sample text:```The GITT model performs better than an empirical lag-time model [17], however, its predicted profiles tend to underestimate Cmax, which, if consistent, may suggest confounding systematic bias or invalid assumptions.```
Sample intent: background



Sample text:```This seems to be the main drawback of the current implementation in comparison to the method of Black Jepson (“refined” in Table 1).```
Sample intent: result



Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method
Sample text:```The adopted theoretical formulation expands on the USVA theory for two confocal beams in the case of lossless media [4,5], by also including absorption in the analytical model.```
Sample intent: method



Sample text:```Following Baker et al. (2012), I include a constant and a linear time trend in xt.```
Sample intent: background



Sample text:```The lower abundance of fish at impacted reaches detected in our study agrees with the results of other authors, who in some cases found four times higher biomass at control reaches (Kube cka et al. 1997; Almod ovar & Nicola 1999).```
Sample intent: result



Sample text:```These models are not specified and observe a single phenomenon from different points of view (Maxfield et al., 2002; Parker et al., 2009).```
Sample intent: method



Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```The GITT model performs better than an empirical lag-time model [17], however, its predicted profiles tend to underestimate Cmax, which, if consistent, may suggest confounding systematic bias or invalid assumptions.```
Sample intent: background



Sample text:```For seeking the optimal value of Wh of the sub-problems which corresponds to the h-th level, we can solve the problem by the smoothing proximal gradient (SPG) method [6] fortunately.```
Sample intent: background



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```Estimation of overall mortality ratio (relative risk) was carried out using the random e ects model according to DerSimonian and Laird [9].```
Sample intent: background



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method