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We consider three variants of our G-hive algorithm. <|MaskedSetence|> <|MaskedSetence|> These two oracle type estimators illustrate the impact of estimating KK with K^\hat{K} and the impact of estimating PB⟂P_{B}^{\perp} with P^B⟂\hat{P}_{B}^{\perp} in our method. <|MaskedSetence|> This is denoted as naive mle and ... | **A**: data driven G-hive is the proposed method that uses a data driven estimate of KK mentioned in Remark 1.
**B**: The other two are oracle type estimators, Oracle(K) G-hive, corresponding to the algorithm with the true value of KK given and Oracle(P) G-hive, the algorithm with the true projection matrix PBP_{B} gi... | ABC | ABC | CBA | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> These tasks are motivated by real-world applications in image recognition, biology, finance, etc. For each example and each learning method, we evaluate the performance of the proposed methods and compare them to those without feature augmentations. <|MaskedSetence|> The objective... | **A**: We emphasize that the proposed feature augmentation framework is broadly applicable across diverse problem domains and learning algorithms.
**B**: To demonstrate its general effectiveness in improving estimation performance, we complement the main study on Chinese news text data with a series of extensive exper... | ABC | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> One widely adopted design in phase II settings is the Bayesian Optimal Phase II (BOP2) design, proposed by Zhou et al. (2020), which is appreciated for its simplicity, efficiency, and ability to control type I error while maximizing power. <|MaskedSetence|> Applying standard BOP2 to such settings ma... | **A**: While this framework provides valuable insight for trial planning, it does not offer a fully operational design for conducting trials with interim monitoring and decision-making under DTE.
**B**: However, the original BOP2 formulation assumes the proportional hazards—assumption that may not hold in trials invol... | ABC | ABC | ABC | ABC | Selection 2 |
Here, we consider methods for componentwise inference for variance components in the presence of nuisance parameters. <|MaskedSetence|> Consequently, the proposed tests and confidence intervals are uniformly valid in finite samples, regardless of how close to a soft or hard boundary the parameter is. <|MaskedSetence... | **A**: We describe universal inference and its application to our setting in more detail in Section 2.
**B**: Thus, even when there are no nuisance parameters, the methods studied here can be preferable to common ones, which are often motivated by asymptotic theory.
**C**: The methods are based on universal inference... | CBA | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> This is done with a probit link, implemented using the ideas of normal latent variables/data augmentation from Albert and Chib, (1993), akin to how Chipman et al., (2010) created the probit-variant of original BART.
The paper proceeds as follows. <|MaskedSetence|> <|MaskedSetence|> In Section 4 w... | **A**: In Section 3 we propose probit monotone BART, including the model set up and some details on the code implementation.
**B**:
In this paper, we introduce an implementation of monotone BART for binary outcomes.
**C**: We first review the original, probit, and monotone BART models from the literature.
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The structure of the paper is as follows. <|MaskedSetence|> In Section 3, we examine commonly used accuracy metrics related to the study of individual mobility. These include jump length, radius of gyration, mean-squared displacement, and measures of frequented locations. In Section 4 we review previous research on i... | **A**: In Section 5
we introduce the LFCM together with a Bayesian framework for inference and estimation of this model.
**B**: In Section 2 we describe the characteristics of contemporary mobile device GPS data, specifically referring to GPS-generated geolocations collected at nonuniform time intervals from 293 anony... | BAC | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> 22&ZD158 and 22VRCO49) and Project of ”XingDianYingCai” Plan of Yunnan Province to L. S. and JSPS Postdoctoral Fellowship Program for Foreign Researchers (No. <|MaskedSetence|> | **A**:
This work was supported by National Natural Science Foundation of China (NSFC, Grant No.
**B**: 12271471, 11931015), National Social Science Foundation of China (Grants Nos.
**C**: P21374) and an accompanying Grant-in-Aid for Scientific Research to C. S.
Appendix.
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(a version of the fixed point iteration for ψα\psi_{\alpha}) is also used by the codes in [10] to compute the empirical expectiles. This was probably not realized in [6] since it was not stated in the description/manual of [10]. On the other hand, [6] gives an interpretation of the fixed point iteration for ψα\psi_{\a... | **A**: 2.3] provides quadratic convergence as expected from a Newton-type method..
**B**: 2.2, Cor.
**C**: Under additional assumptions (the distribution has a continuous density function), [6, Thm.
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<|MaskedSetence|> <|MaskedSetence|> Moreover, we report on the results of a small Monte Carlo simulation study. <|MaskedSetence|> In the last section, the basic idea is carried over to the two-sided alternative H1∗:F≠GH_{1}^{*}:F\neq G. For the corresponding transformation, we also determine the limit distribution..... | **A**: In section 3 it is shown that nm(n+m)Tnm\sqrt{nm(n+m)}T_{nm} converges in distribution to a random variable ZZ, which follows the Maxwell-Boltzmann law.
**B**: By Remark 1 the event {Rnm=n+m}\{R_{nm}=n+m\} has positive probability m(n+m)(n+m−1)\frac{m}{(n+m)(n+m-1)} and therefore cannot be ignored.
The... | BAC | CBA | BAC | BAC | Selection 3 |
<|MaskedSetence|> This is largely due to the expensive computational cost associated with Markov chain Monte Carlo (MCMC) sampling methods. <|MaskedSetence|> Unlike MCMC, VI is optimization-based, which tends to be faster while still having comparable numerical performance. VI methods have been gaining rapid developm... | **A**: Readers are referred to Bhattacharya et al., (2025); Katsevich and Rigollet, (2024); Zhang and Yang, (2024); Wang and Blei, (2019); Han and Yang, (2019); Hinton and van Camp, (1993); Jordan et al., (1998); Peterson and Anderson, (1987) and references therein for the recent advances of VI methods in general.
**B... | CBA | CBA | BCA | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> These principal components serve as low-dimensional representations that are subsequently used as covariates in a regression model. However, while this approach is widely used in applications (Liang et al., 2015; Chen et al., 2024; Iao et al., 2024; Zhou et al., 2024), it may not a... | **A**: FLM is often implemented using an FPCA-based approach (Yao et al., 2005b; Hall and Hosseini-Nasab, 2006; Hilgert et al., 2013; Imaizumi and Kato, 2018), where FPCA is first applied to decompose functional predictors into orthogonal principal components.
**B**: These considerations motivate the development of al... | CAB | CAB | CAB | CBA | Selection 2 |
ProCause also extends RealCause by integrating LSTM-based learners. Our results indicate that LSTMs can improve evaluation accuracy for PresPM methods with time-dependent treatments, such as the time contact HQ intervention. <|MaskedSetence|> <|MaskedSetence|> Therefore, the choice between LSTM and MLP should be gui... | **A**: For the fixed intervention (set interest rate), MLP-based models tend to be more stable.
**B**: On the other hand, even small but uneven absolute errors across the PresPM methods evaluated can distort the rankings, resulting in strong absolute but poor relative performance.
**C**: However, these improvements a... | CAB | ACB | CAB | CAB | Selection 3 |
In this paper, we propose a novel Causal SHAP framework that explains how each feature in the dataset contributes to the model’s prediction while respecting the causal relationships within the data. <|MaskedSetence|> <|MaskedSetence|> IDA builds upon the directed acyclic graphs (DAGs) inferred by PC to estimate causa... | **A**: Specifically, our approach integrates causal relationships into SHAP calculations by employing the Peter-Clark (PC) algorithm [6] for causal edge discovery and the Intervention Calculus when the DAG is Absent (IDA) algorithm [7] for causal strength quantification.
**B**: The PC algorithm is a constraint-based m... | ABC | ABC | ABC | BAC | Selection 1 |
<|MaskedSetence|> However, they offer no guidance on how to identify such a model from data, nor do they specify its size or neuronal structure.
Guarantees based on constructive approximation, especially [113, 88, 16, 50, 101, 96], improve upon this by providing estimates for the number of neurons and their arrangem... | **A**: While these approaches achieve minimax-optimal approximation rates, as dictated by matching lower-bounds in constructive approximation theory [74] and Vapnik-Chervonenkis theory [113, 101], they rely on precisely placed samples without measurement noise.
**B**: Classical existence theorems, i.e., the classical ... | BAC | BAC | BAC | BCA | Selection 2 |
To address these challenges, we present a novel hybrid pipeline that bridges semantic topic modeling and graph‐based representation learning to cluster legal documents in an unsupervised manner. First, we employ Top2Vec (Angelov, 2020), which jointly learns document, word, and topic vectors, allowing automatic determin... | **A**: Visualization of these high-dimensional structures is achieved using Uniform Manifold Approximation and Projection (UMAP), facilitating intuitive inspection of the resultant clusters.
.
**B**: We demonstrate the efficacy of our pipeline on two distinct legal corpora: the Atticus Clause Retrieval Dataset (ACORD)... | CBA | CAB | CBA | CBA | Selection 1 |
This mirrors expression (II.1), but with one crucial difference: in order for SS to be an e-variable, the denominator P0w0P_{0}^{w_{0}} cannot be chosen freely, as it must be the prior w0∗w_{0}^{*} that ensures that SS qualifies as a GRO e-variable (i.e., satisfies the e-variable condition).
This formulation, however... | **A**: Extending this insight to multiple models remains an important open challenge..
**B**: This can be seen as the natural solution to the problem — at least for the two-model comparison case [31].
**C**: 9, page 413].
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V-D1 Synthetic Data Visualization
Figure 5 shows the visualization results of five PreQ-NegP-R models on synthetic data. <|MaskedSetence|> ER represents the theoretical ranking of the model. <|MaskedSetence|> <|MaskedSetence|> eTaPR and Aff-F1 also have estimation inaccuracies.
. | **A**: In this case, only CCE and VUS-ROC can obtain the correct ranking of model performance.
**B**: Among them, Reduced-F1 has the worst estimation ability, and the score of PreQ0.9-NegP0.1-R, which is expected to rank third, is actually the highest.
**C**: Their parameters are shown in the first column of Table II... | CAB | CAB | ACB | CAB | Selection 1 |
Tables 2 and 2 present the accuracy and model consistency, respectively, of all methods on the linear synthetic dataset. Our method, NP2M2, achieves the highest accuracy in 4 out of 6 settings and ranks the second in the remaining two, as shown in Tables 2. <|MaskedSetence|> In the two settings where RRM NN achieves ... | **A**: Conversely, when 𝒟(θ)\mathcal{D}(\theta) is more sensitive (d=5d=5), NP2M2 attains the highest accuracy by actively adapting to the sensitive distribution map through appropriate model deployment, ensuring performative stability throughout the iterations.
Tables 4 and 4 present the accuracy and model consist... | CAB | CAB | CAB | BAC | Selection 3 |
Through extensive experiments on multiple datasets, we can better understand the scenarios in which ADMP-GNN proves to be effective. As observed in Tables 3, 4, a comparison between ADMP-GCN and ADMP-GIN against their respective baselines, GCN and GIN, demonstrates consistently higher accuracy for most datasets. Regard... | **A**: For example, in datasets like Cora, ogbn-arxiv, and Photo, the kk-core centrality effectively highlights clusters, enabling an adaptive depth policy.
**B**: For instance, while PageRank is widely used, its distribution often forms a peak near zero due to the normalization constraint where the sum of PageRank sc... | CBA | ACB | CBA | CBA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> We refer to such a point as a positive change point. <|MaskedSetence|> We refer to this as a negative change point. Define the sign feature CPtCP_{t} as follows.
. | **A**: Specifically, a change point where Ct>hC_{t}>h signifies an increase in the player’s winning probability.
**B**: Conversely, a change point where Ct<−hC_{t}<-h indicates a decrease in the player’s winning probability.
**C**:
When the absolute value of CtC_{t} exceeds the predefined positive threshold hh, po... | CAB | CAB | CAB | CAB | Selection 2 |
In Figure.1, we provide the recovery performance as a function of the sparsity level KK. <|MaskedSetence|> <|MaskedSetence|> As the external Gaussian noise is introduced (SNR↓\ \downarrow) the reconstruction performance of gOMP starts to deteriorate. But JS-gOMP gives the best results in reduced noise in the reconstr... | **A**: As more unnecessary components start to appear in the output making the signal less sparse, the relative contribution of the actual atoms in the signal tends to reduce in both cases (OMP, gOMP), whereas the JS-gOMP gives considerably better results in reducing the noise (as per Figure.
**B**: A higher level of ... | BCA | CAB | BCA | BCA | Selection 3 |
Prominent applications of log-linear regression include the modeling of trade data and panel data with non-negative outcomes, such as earnings (Card, 2001), and count and count-like data in finance, such as number of corporate patents granted (Hirshleifer et al., 2012) and firms’ toxic waste release volumes (Akey an... | **A**: For example, Cohn et al.
**B**: However, previous works have raised some important drawbacks of the log-linear regression approach.
**C**: Similar issues with other non-linear transformations of non-negative outcome variables, such as the inverse hyperbolic sine (IHS),
have been studied by Mullahy (1998), Mann... | BAC | BAC | CAB | BAC | Selection 4 |
While these models offer a principled approach to capture spatio-temporal dependencies, they come with notable limitations. A major drawback is that they often struggle to represent localized temporal trends while preserving appropriate smoothing across space and time (Rushworth et al., 2017). This issue often stems f... | **A**: Specifically, we impose a CAR structure on the GPs’ variance parameters, and a conditional dependence of the temporal range on the temporal variability, ensuring that nearby regions share information in a flexible and data-driven way.
**B**: GPs offer an elegant framework for capturing complex and temporal depe... | CBA | CBA | BAC | CBA | Selection 4 |
Score-based methods, on the other hand, optimize for a certain score (e.g. Bayesian Information Criterion) to recover the causal structure. Those scores represent how well the discovered structure can model the data, and incorporate further constraints (i.e. <|MaskedSetence|> for the total number of edges in the foun... | **A**: DAGness) and penalties (e.g.
**B**: (2022) aim to learn the true graph using discrete backpropagation and the same regularization as Zheng et al.
**C**: Traditionally, score-based methods search discretely through the space of possible graph structures.
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<|MaskedSetence|> These models reflect an active line of research focused on enhancing DeepONet’s expressive capacity through architectural innovations.
Specifically, to improve the predictive accuracy of the “vanilla” DeepONet, a modified DeepONet architecture with increased structural complexity was proposed in prio... | **A**: In response to this architectural limitation, various extensions have been proposed to improve representational capacity, including SVD-DeepONet [31], the R-adaptive DeepONet designed for discontinuities [41], multi-input EDeepONet [29], and the Separable Operator Networks [40].
**B**: Motivated by these develo... | ACB | ACB | CBA | ACB | Selection 1 |
Table 1 plots the peak signal-to-noise ratio (PSNR) of the particle means, compared to the MAP estimate Eq. 48 (equivalently, the minimizer of the potential). <|MaskedSetence|> Moreover, PBRWP has higher PSNR at lower iterations, indicating faster convergence. <|MaskedSetence|> Figure 10 plots the pixel-wise stand... | **A**: The lower PSNR of the preconditioned methods compared to the non-preconditioned methods can be explained by the differing noise and diffusion scales.
**B**: Moreover, the noise-free methods have very low noise outside the edges.
**C**: We observe that both BRWP and PBRWP converge to much higher values of PSNR,... | CAB | CAB | CAB | BCA | Selection 2 |
In the current application, gNg_{N} is an estimated regression function that indexes the target parameter. We will impose Donsker conditions on this estimator. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We, therefore, do not consider cross-fitting in this paper.
. | **A**: In the non-parametric literature, cross-fitting is a popular approach to avoid Donsker conditions [[, see, e.g.,]]chernozhukov2018double.
**B**: Methods for inference about data-adaptive target parameters that use cross-fitting exist (see, e.g., [98, Section 7]), but target different types of data-adaptive para... | CAB | CAB | CAB | ABC | Selection 1 |
Results. We summarize the results across the two simulations in Fig.˜2. <|MaskedSetence|> By contrast, the baseline methods frequently fall far short of nominal coverage: in the second simulation, all baselines achieve zero coverage for certain instances. <|MaskedSetence|> The cost of this conservativeness is wider c... | **A**: This failure is accompanied by high rates of false positives, meaning the baselines often return intervals that confidently — but incorrectly — assign the wrong sign to the association.
The strength of our method lies in its reliability: it avoids misleading conclusions even in challenging extrapolation regime... | BAC | BAC | BCA | BAC | Selection 4 |
The definition of “recovery” is not the same. The necessity statement (i) is about the impossibility of exact recovery (i.e. equality of supports), which is stronger than approximate recovery (i.e. vanishing rescaled error) that the sufficiency statement (ii) ensures. <|MaskedSetence|> To make the phase transition cle... | **A**: In fact, the same sequence of proofs happened in the dense setting literature, where Gamarnik and Zadik gamarnik2022sparse showed approximate recovery, followed by Reeves et al.
**B**: We believe the same holds in our sparse setting and leave the proof for future work.
.
**C**: In particular, it is possibl... | CAB | CAB | CAB | ACB | Selection 3 |
Our approach is based on strong simplifying assumptions that may need to be addressed before considering specific public health applications. <|MaskedSetence|> <|MaskedSetence|> These probabilities could be used as priors in Eq. (8) [14], leading to more accurate estimations of both trees and parameters based on th... | **A**: Importantly, many of these limitations can be addressed as extension and generalizations of the framework proposed here.
**B**: For instance, we considered all parameters θ\theta of our model fixed, while a more realistic setting would be to specify probabilities for different parameter values P(θ)P(\theta).
... | ABC | ABC | ABC | ACB | Selection 1 |
<|MaskedSetence|> Kocaoglu et al. (2017c) proposes a stage-wise algorithm to address the bidirected edges induced by latent confounders, introducing the do-see test to determine whether a particular bidirected edge exists. <|MaskedSetence|> (2020) also considers confounders but focuses on minimizing the total interve... | **A**: To date, this remains the only study that addresses experimental design for causal structure learning in cyclic graphs.
Research on the second class of problems—designing interventions under a fixed budget to minimize post-intervention uncertainty—has thus far focused exclusively on settings without confounder... | CAB | BCA | BCA | BCA | Selection 3 |
3 Identification of Path Specific Effects under Selection Bias
The identification of mediation quantities (including direct/indirect effects and path-specific effects) depends on additional assumptions beyond those encoded directly in the graphical model GG. <|MaskedSetence|> (2019); Didelez (2019); Robins et al. (20... | **A**: See Robins et al.
**B**: In particular, there are three distinct causal models that have figured prominently in discussions of mediation analysis: the NPSEM-IE (nonparametric structural equations model with independent errors a.k.a. the “multiple worlds model”), the FFRCISTG model of Robins (Robins, 1986), and ... | CAB | BAC | BAC | BAC | Selection 2 |
The geostatistical weeds monitoring data were assessed in the field and collated by the ACT using a modified Braun-Blanquet system (see Table 1 in the Supplementary Information provided as a vignette to the supporting R package DSTSOM, Hosack et al.,, 2025). Higher scores that correspond with high percent cover were ra... | **A**: These plots are located in designated areas of the ACT that have high conservation value and are actively managed to offset potential environmental impacts from development activities located elsewhere.
**B**: The 1,299 observations were inhomogeneously distributed through the ACT (Figure 1) because scoring of ... | BAC | BAC | BCA | BAC | Selection 2 |
<|MaskedSetence|> RSSampling supports a wide range of RSS extensions, offering both sampling tools and statistical inference. <|MaskedSetence|> RSStest focuses on sampling and mean testing for RSS and MRSS and includes simulation tools under normal distributions. Finally, RankedSetSampling extends its scope to joint ... | **A**: URSS offers a flexible alternative by allowing unequal allocation across strata, making it particularly effective for skewed distributions where unequal allocation can significantly improve estimation efficiency.
**B**: Several R packages are available for RSS, including RSSampling [24], NSM3 [23], RSStest [16]... | BCA | BCA | ACB | BCA | Selection 2 |
Figure 5 illustrates the prevalence of identity slippage and distribution of claim types across sections and disciplines. <|MaskedSetence|> Within economics, identity slippage occurred at least once in 60 studies (68%). Quantitatively, prevalence was comparable in political science science (n = 42, 69%), and somewhat... | **A**: We found identity slippage to be widespread across disciplines.
**B**: For example, type (a) claims—arguably the clearest reference to the local nature of ΨLATE\Psi_{LATE}—were far more common in economics than in epidemiology, where type (b) claims predominated.
**C**: A more detailed cross-discipline anal... | CBA | ABC | ABC | ABC | Selection 2 |
In the third simulation, data are generated under a high-dimensional setting with 500 biomarkers. <|MaskedSetence|> <|MaskedSetence|> Additional structured perturbations are introduced to the latter 490 biomarkers to increase the difficulty of variable selection. The disease status is generated by applying a comple... | **A**: We consider total sample sizes of 200, 400, and 600, ensuring that in all cases the training sample size is smaller than the number of biomarkers.
.
**B**: The biomarkers are generated from a multivariate Gaussian distribution with an AR(1) correlation structure.
**C**: The true coefficient vector assigns non... | CBA | CBA | BCA | CBA | Selection 1 |
In current machine learning, pattern discovery or association rule learning represents an important task [Golden (2020)]. <|MaskedSetence|> (2017)] or to search for latent clusters in [Li
et al. (2022)]. <|MaskedSetence|> <|MaskedSetence|> | **A**: (2020)]
for the integration of genomics or metabolomics data.
It is therefore natural to assume that effective pattern discovery in contingency tables should rely on suitable regularized estimation tools, as these are able to stabilize inference in the presence of sparse counts and improve the reliability of det... | CBA | CBA | CBA | BCA | Selection 3 |
The rest of the paper is organized as follows. <|MaskedSetence|> In Section 3, we introduce the main focus: completely monotone positive geometries. Subsection 3.1 briefly discusses a related weaker notion, positive convexity. Subsection 3.2 connects complete monotonicity of rational functions to hyperbolic polynomi... | **A**: Section 2 reviews essential background on projective and convex geometry, positive geometries, and the concept of complete monotonicity including the Hausdorff-Widder-Choquet theorem.
**B**: Finally, Section 5 outlines open questions for future research.
.
**C**: Subsection 4.5 focuses on the measure for a po... | BAC | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> The Koutseff [4] and Cornec [12] datasets include additional information, if the cry is a cry of pain (cry during vaccine) or not (cry during bath), plus the type of vaccine for the Koutseff dataset. All these datasets will be made freely accessible in an upcoming publication, curr... | **A**: CryCeleb [15] provides labels associated with the child's identity and age (birth or discharge).
Given the results of these previous studies, we expect no significant information related to sexes or causes.
**B**: The Bouchet, Lefkir, and Vial datasets provide information on identity and sex.
**C**: Of these... | BAC | BCA | BCA | BCA | Selection 4 |
•
Imagine that a decision maker is using a predictor to make decisions that minimize their own loss function. <|MaskedSetence|> For instance, we could use forecasts about rain to decide whether or not to carry an umbrella, to decide whether to have a party outdoors or indoors, or whether to turn off the sprinklers. ... | **A**: This loss may differ from the one used to train the model, and might differ across various decision makers.
**B**: Say our loss for carrying an umbrella when it does not rain is 0.10.1, and for not carrying an umbrella when it rains is 0.90.9.
**C**: Each of these has its own loss function.
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<|MaskedSetence|> To highlight the type of inference enabled by the Poseidon formulation, we focus on the results obtained for three CCs associated with biologically meaningful tissue regions. <|MaskedSetence|> Two correspond to distinct areas within the tumor nodule, depicted in the top (Cluster A) and bottom (Clust... | **A**: These CCs are reported in the left panels of Figure 6.
**B**: More importantly, we can analyze the behavior of the m/z values across the three highlighted clusters.
**C**: Results on m/z in specific CCs.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> From a practical perspective, the choice between PN and w-PN must consider computational and data requirements. While PN provides stronger discriminatory power, its inference involves substantially higher computational costs compared to the intervention-based w-P... | **A**: However, to address underlying conditions triggering successive failures, necessity measures provide a more robust analytical foundation.
Accordingly, the consistent capability of necessity-based metrics to accurately recover the true causal paths underscores their adequacy for automated RCA in intelligent aut... | CBA | CBA | ABC | CBA | Selection 4 |
LHS was first introduced in the 1970s [5, 6] and was further developed to optimize projection properties, improve space-filling design, and avoid spurious correlations [7, 8, 9, 10, 11, 12, 13, 14]. <|MaskedSetence|> <|MaskedSetence|> LHS distributes points to tile the space while preserving the one-dimensional proj... | **A**: However, these are typically single-stage sampling algorithms, requiring all samples to be drawn simultaneously or, at most, in a finite number of discrete steps.
**B**: A Latin hypercube generalizes this property to a PP-dimensional hypercube, where each dimension is binned into NN disjoint intervals [i/N,(i+1... | CBA | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> This variable selection task can be facilitated via LASSO (Leluc et al., 2021) but can be prohibitively slow. A simpler approach, without relying on penalised regression techniques, is to randomly select J∗<SJ^{*}<S columns without replacement from Z, represented by a random selection operator 𝕊\mat... | **A**: We refer to our proposed method as the ensemble ZVCV algorithm (Algorithm 2).
.
**B**:
This is equivalent to subsetting the ZVCV design matrix Z by column indicated by the mapping 𝕊\mathbb{S} and estimating the coefficients corresponding to the selected covariates via the OLS criterion.
**C**: If J∗≥JJ^{*}... | BCA | BAC | BCA | BCA | Selection 3 |
Figure 1: Non‐zero histograms for six individual NME types
We model six individual NMEs Ci,t(e)C^{(e)}_{i,t}: harsh braking, harsh acceleration, serious speeding, forward collision, lane departure, too close distance and their combination NMEs Ni,tN_{i,t}. The exposure is the weekly total distance EiE_{i}; all model... | **A**: Each round uses four folds for training and one for testing; metrics are averaged over folds and reported with standard deviations.
**B**: For the dataset, all train/test splits are performed at the driver level.
**C**: The NME histograms are shown in Figure 1, which shows excess zeros and a long-tail distribu... | ABC | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> Through the integration of theoretical derivations, simulation experiments, and an applied analysis of ovarian cancer data, we demonstrated how these estimators can be used together to achieve robust shape recovery, predictive accuracy, and principled uncertainty quantification.
The nonparametric E... | **A**: This study provides a comprehensive evaluation of three complementary approaches for interval-censored survival analysis: nonparametric EM-based NPMLE, parametric accelerated failure time (AFT) models, and Bayesian AFT formulations.
**B**: Although jagged in appearance, this stepwise form is faithful to the unc... | ACB | ABC | ACB | ACB | Selection 1 |
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