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The Balance-Scale Task Revisited: A Comparison of Statistical Models for Rule-Based and Information-Integration Theories of Proportional Reasoning

Abe D. Hofman, Ingmar Visser, Brenda R. J. Jansen, Han L. J. van der Maas, Tom Verguts
2015 PLoS ONE  
We propose and test three statistical models for the analysis of children's responses to the balance scale task, a seminal task to study proportional reasoning.  ...  We use a latent class modelling approach to formulate a rule-based latent class model (RB LCM) following from a rule-based perspective on proportional reasoning and a new statistical model, the Weighted  ...  In this study, we consider conflict items of the type conflict-balance-addition (CBA), conflict-weight-addition (CWA) and conflict-distance-addition (CDA), next to conflict-balance (CB), conflict-distance  ... 
doi:10.1371/journal.pone.0136449 pmid:26505905 pmcid:PMC4623502 fatcat:bnrxgzniezalza5sh3t5trhbim

The Development of Children's Rule Use on the Balance Scale Task

Brenda R.J Jansen, Han L.J van der Maas
2002 Journal of Experimental Child Psychology  
A model for the development of reasoning about the balance scale task is proposed.  ...  The model is a restricted form of the overlapping waves model (Siegler, 1996) and predicts both discontinuous and gradual transitions between rules. © 2002 Elsevier Science (USA)  ...  The construction of latent class models for responses to balance scale items is further explained under Method after a description of the design and the administration of the balance scale test.  ... 
doi:10.1006/jecp.2002.2664 pmid:11890728 fatcat:jidrdw36lrflfmssoh7lzbbqva

Residual Balancing: A Method of Constructing Weights for Marginal Structural Models [article]

Xiang Zhou, Geoffrey T. Wodtke
2019 arXiv   pre-print
To avoid such bias, researchers often use marginal structural models (MSMs) with inverse probability weighting (IPW).  ...  We introduce an alternative method of constructing weights for MSMs, which we call "residual balancing."  ...  Similarly, the stabilized inverse probability weights are here defined as balancing for estimating marginal effects with (a) a binary time-varying treatment under correct model specification, (b) a continuous  ... 
arXiv:1807.10869v2 fatcat:m47qkhu63fa7behcksbexfkgy4

Stable IPW estimation for longitudinal studies

Avagyan Vahe, Stijn Vansteelandt
2021 Scandinavian Journal of Statistics  
We consider estimation of the average effect of time-varying dichotomous exposure on outcome using inverse probability weighting (IPW) under the assumption that there is no unmeasured confounding of the  ...  Our proposed approach targets the estimation of the counterfactual mean under a chosen treatment regime and requires fitting a separate propensity score model at each time point.  ...  ) under the model defined by the restrictions on the propensity score.  ... 
doi:10.1111/sjos.12542 fatcat:rusjb6rxmnfz5d6x4eskreiv4m

Quantitative dynamics of adipose cells

Junghyo Jo, Zeina Shreif, Vipul Periwal
2012 Adipocyte  
In addition to the physical characteristics of adipose cells, quantitative modeling integrates dynamics of adipose cells, providing the mechanism of cell turnover under normal and drug-treated conditions  ...  One interesting finding is that the growth/shrinkage of adipose cells (. 50 mm diameter) under positive/negative energy balance is proportional to the surface area of cells, limiting efficient lipid absorption  ...  Acknowledgments This work was supported by funding from the intramural research program of the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.  ... 
doi:10.4161/adip.19705 pmid:23700516 pmcid:PMC3609080 fatcat:zxgdgom4gvgivamvvfxuh6qoxu

An introduction to inverse probability of treatment weighting in observational research

Nicholas C Chesnaye, Vianda S Stel, Giovanni Tripepi, Friedo W Dekker, Edouard L Fu, Carmine Zoccali, Kitty J Jager
2021 Clinical Kidney Journal  
Second, weights are calculated as the inverse of the propensity score.  ...  In this article we introduce the concept of inverse probability of treatment weighting (IPTW) and describe how this method can be applied to adjust for measured confounding in observational research, illustrated  ...  In addition, as we expect the effect of age on the probability of EHD will be non-linear, we include a cubic spline for age.  ... 
doi:10.1093/ckj/sfab158 pmid:35035932 pmcid:PMC8757413 fatcat:4h6ezvhbdjavzobwxkdrv3jmly

The Right Tool for the Job

Claude M. Setodji, Daniel F. McCaffrey, Lane F. Burgette, Daniel Almirall, Beth Ann Griffin
2017 Epidemiology  
We found that when non-complex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted  ...  In this article, we compare two promising propensity score estimation methods (for time invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models  ...  Acknowledgments Funding Source: All phases of this study were supported by an NIH grant R01DA034065 from the National Institute on Drug Abuse (NIDA) and a funding from the RAND center for causal inference  ... 
doi:10.1097/ede.0000000000000734 pmid:28817469 pmcid:PMC5617809 fatcat:dkiroy6345ch5k3digfinreyg4

Efficient Covariate Balancing for the Local Average Treatment Effect [article]

Phillip Heiler
2020 arXiv   pre-print
probability weighting methods.  ...  The method weighs both treatment and outcome information with inverse probabilities to produce exact finite sample balance across instrument level groups.  ...  The fundamental mechanism behind identification via inverse probability weighting is the balancing property, i.e. the inverse probability weights will balance the distribution of any function of covariates  ... 
arXiv:2007.04346v1 fatcat:lgi66iuyjzeutnx47pk7lsd2ze

Statistical Test of the Rule Assessment Methodology by Latent Class Analysis

Brenda R.J. Jansen, Han L.J. van der Maas
1997 Developmental Review  
According to McClelland and Jenkins (1991) , this connectionistic model simulates the saltatory acquisition of rules for tasks like the balance scale.  ...  The latent class models do not fit the PDP data, indicating that the behavior of the PDP model can not be characterized by rules.  ...  In the sixth and seventh column the expected probabilities for the rule models buggy-rule (buggy) and addition-rule (add) are displayed. a The conflict-balance item as well as the buggy conflict-balance  ... 
doi:10.1006/drev.1997.0437 fatcat:oxxmilh2gjcevfwd4wkrwnllai

Adjustment for Biased Sampling Using NHANES Derived Propensity Weights [article]

Olivia M. Bernstein, Brian G. Vegetabile, Christian R. Salazar, Joshua D. Grill, Daniel L. Gillen
2021 arXiv   pre-print
the probability of membership in C2C relative to NHANES.  ...  Simulation studies explore the impact of propensity weight estimation on uncertainty.  ...  JDG and DLG were supported by the National Institutes of Health under award P30AG066519. DLG was also supported by the National Institute of Health under award R01AG053555.  ... 
arXiv:2104.10298v1 fatcat:ti3bqqtvvfgxbp564nw2j2owwa

IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance [article]

Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson
2021 arXiv   pre-print
IB-GAN is simple to train and model-agnostic, pairing any deep learning classifier with a generator-discriminator duo and resulting in higher accuracy for under-observed classes.  ...  We propose Imputation Balanced GAN (IB-GAN), a novel method that joins data augmentation and classification in a one-step process via an imputation-balancing approach.  ...  Let the prior label probability of the additional data distribution to be w y = P (Y = y) for y ∈ Y and the conditional probability p y (x) := P (X = x|Y = y).  ... 
arXiv:2110.07460v1 fatcat:e2b2ddoy7valhbfxtzrlzijaem

Bias-robustness and efficiency of model-based inference in survey sampling

Desislava Nedyalkova, Yves Tille
2012 Statistica sinica  
We show the value of selecting a balanced sample with inclusion probabilities proportional to the standard deviations of the errors with the Horvitz-Thompson estimator.  ...  In model-based inference, the selection of balanced samples has been considered to give protection against misspecification of the model.  ...  Acknowledgement The authors thank two anonymous reviewers and the Editor for their constructive comments that helped us improve the quality of this paper.  ... 
doi:10.5705/ss.2010.238 fatcat:joqt5qlqejgvllx4alorlhk2ky

Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data

José R. Zubizarreta
2015 Journal of the American Statistical Association  
balance adjustment, answering the question, how much does tightening a balance constraint increases the variance of the weights?  ...  Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data.  ...  Proposition 4.2 below bounds the bias under a generalized additive regression form by balancing auxiliary covariates ∼ X k i,j,p , which are a transformation of the original covariates X i,p .  ... 
doi:10.1080/01621459.2015.1023805 fatcat:zx6rhjzri5dn7allujm76jss6y

An EC Services System Using Evolutionary Algorithm [chapter]

Whe Dar Lin
2004 Lecture Notes in Computer Science  
To create a better algorithm, we have analyzed a variety of transaction schemes compatible with standards and developed a modeling framework on which maintaining good consistency.  ...  Our algorithm resolves the concurrent data-accessing problem among EC services databases.  ...  The appropriate setting for the communication delay of the real time transactions can meet their loading balance value on time under the simulation results.  ... 
doi:10.1007/978-3-540-24685-5_87 fatcat:5otlfwzl6jfmncbmocbquz2k7y

Propensity Score Weighting for Covariate Adjustment in Randomized Clinical Trials [article]

Shuxi Zeng, Fan Li, Rui Wang, Fan Li
2020 arXiv   pre-print
An objective alternative is through inverse probability weighting (IPW) of the propensity scores.  ...  In this article, we point out that IPW is a special case of the general class of balancing weights, and advocate to use overlap weighting (OW) for covariate adjustment.  ...  Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH.  ... 
arXiv:2004.10075v2 fatcat:ttvtt5bj3jgmzo3wvi4clxqqem
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