標(biāo)題: Titlebook: Dependent Data in Social Sciences Research; Forms, Issues, and M Mark Stemmler,Wolfgang Wiedermann,Francis L. Huang Book 2024Latest edition [打印本頁] 作者: irritants 時(shí)間: 2025-3-21 18:18
書目名稱Dependent Data in Social Sciences Research影響因子(影響力)
書目名稱Dependent Data in Social Sciences Research影響因子(影響力)學(xué)科排名
書目名稱Dependent Data in Social Sciences Research網(wǎng)絡(luò)公開度
書目名稱Dependent Data in Social Sciences Research網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Dependent Data in Social Sciences Research被引頻次
書目名稱Dependent Data in Social Sciences Research被引頻次學(xué)科排名
書目名稱Dependent Data in Social Sciences Research年度引用
書目名稱Dependent Data in Social Sciences Research年度引用學(xué)科排名
書目名稱Dependent Data in Social Sciences Research讀者反饋
書目名稱Dependent Data in Social Sciences Research讀者反饋學(xué)科排名
作者: 一大塊 時(shí)間: 2025-3-21 22:15 作者: SLAG 時(shí)間: 2025-3-22 02:48 作者: PACK 時(shí)間: 2025-3-22 05:44 作者: 代替 時(shí)間: 2025-3-22 09:35 作者: 失望昨天 時(shí)間: 2025-3-22 15:57
Exploration of Dependence Structures in Longitudinal Categorical Data with Ordinal Responsesrelationship with categorical covariates, the proposed approach consists of a set of SCCRAM-based strategies that take into account time dependence, data format, potential of asymmetric dependence, and model-free inference. The utility of the proposed method is demonstrated using two longitudinal ca作者: 失望昨天 時(shí)間: 2025-3-22 17:56
Bayesian Network for Discovering the Potential Causal Structure in Observational Dataht on the factors that drive observed patterns and phenomena, facilitating a clear understanding of the intricate web of relationships, enabling researchers and practitioners to derive meaningful insights, and making informed decisions based on a nuanced understanding of the causal mechanisms at pla作者: Mucosa 時(shí)間: 2025-3-23 00:39 作者: 生來 時(shí)間: 2025-3-23 01:57 作者: ORE 時(shí)間: 2025-3-23 08:55 作者: BILK 時(shí)間: 2025-3-23 11:03
Jaitri Das,Buddhadeb Chattopadhyayn models for longitudinal data, focusing on continuous-time (CT) models. Unlike the more widely used discrete-time (DT) models, CT models do not require the time intervals between measurements to be equal and, therefore, can adapt effortlessly to irregular sampling schemes. Thus, our resulting appro作者: 令人作嘔 時(shí)間: 2025-3-23 17:38 作者: 生銹 時(shí)間: 2025-3-23 20:43 作者: 尊敬 時(shí)間: 2025-3-23 23:28
Mauro Ferrario,Maria Clelia Righiearly related to each other, and errors may be multiplicative. Thus, the present chapter discusses linearizable non-linear models for which distributional and independence-based direction dependence measures are applicable. Simulation results suggest that direction of dependence properties of linear作者: Visual-Field 時(shí)間: 2025-3-24 03:34 作者: botany 時(shí)間: 2025-3-24 10:07 作者: ATOPY 時(shí)間: 2025-3-24 13:39 作者: 取消 時(shí)間: 2025-3-24 16:52 作者: CROW 時(shí)間: 2025-3-24 20:28 作者: PACK 時(shí)間: 2025-3-24 23:14
Engineered Fe-Based Nanocolumnar Films,n the construction of analytical models. In this chapter, we look at how longitudinal data are analyzed in latent growth curve models. We focus on the real-world problem of sampling-time variation, when individuals do not have exactly equal intervals between measurements, its consequences, and how t作者: 間諜活動(dòng) 時(shí)間: 2025-3-25 06:23
Jaitri Das,Buddhadeb Chattopadhyayestablished itself as one of the primary tools for the recursive partitioning of structural equation models (SEM). The resulting SEM trees partition the sample into groups of similar individuals while identifying the most important predictors of group differences in the process. However, until recen作者: Truculent 時(shí)間: 2025-3-25 11:23
Climate Change and Agriculture, is often the main direction of influence, there are also bidirectional processes, e.g., as described in the parent-child coercive cycle (cf. Patterson GR, Coercive family process. Castalia, Eugene, 1982). These processes were mainly investigated in clinical and other studies from North America, but作者: STALL 時(shí)間: 2025-3-25 11:56
Electromagnetic Wave Absorption Materials,ine learning. This is a purely time-continuous approach relying on the theory of optimization for dynamical systems. We complement the proposed algorithm with a practical example, comparing the results of this approach to those obtained via Continuous Time Structural Equation Modeling (.). To this e作者: Hippocampus 時(shí)間: 2025-3-25 18:21 作者: carbohydrate 時(shí)間: 2025-3-25 20:35 作者: CARK 時(shí)間: 2025-3-26 02:37
s various tools to study such mechanisms. However, owing to the lack of background knowledge, it is often difficult to prepare causal graphs required for performing statistical causal inference. To alleviate the difficulty, we have worked on developing statistical methods for estimating causal relat作者: 減至最低 時(shí)間: 2025-3-26 04:45 作者: Ophthalmoscope 時(shí)間: 2025-3-26 08:55
Introduction to Manufacturing Engineering,al information on dependence in repeatedly measured outcomes, which may be valuable for building statistical models for explanation and prediction. This paper proposes an explorative approach to facilitate the understanding of dependence structures in longitudinal categorical data with ordinal outco作者: KIN 時(shí)間: 2025-3-26 15:57
Helical, Bevel, and Worm Gears,onal datasets. Based on principles from Bayesian statistics, this approach goes beyond mere pattern recognition, delving into the realm of causation by modeling the probabilistic conditional dependencies among variables. This chapter discusses the logic of using Bayesian network analysis as a causal作者: Assemble 時(shí)間: 2025-3-26 18:13 作者: SUGAR 時(shí)間: 2025-3-27 00:11
Active Targeting of Nanomedicines are nested within classrooms in educational studies, or participants are repeatedly measured at different time points in longitudinal studies. Multilevel models (MLM), also known as hierarchical linear models and mixed effects models, have been widely used to account for clustered data.作者: 牢騷 時(shí)間: 2025-3-27 04:32
Understanding the Budget Process present when observations are nested within two higher-level structures which in turn are not nested within each other. To capture the variations from both clustering dimensions, cross-classified random effects models (CCREMs) were developed as an extension to standard multilevel modeling. Alternat作者: theta-waves 時(shí)間: 2025-3-27 06:03
Xiaoyu Wang,Yufei Wang,Qi Liang,Yuning Zhangtion approach for score-based tests of mixed models, which addresses situations where there is dependence between scores. This differs from the traditional score-based tests, which require independence of scores. We first review traditional score-based tests and then propose a new, self-normalized s作者: tic-douloureux 時(shí)間: 2025-3-27 11:22 作者: 貪婪的人 時(shí)間: 2025-3-27 15:36
Mark Stemmler,Wolfgang Wiedermann,Francis L. HuangPresents new developments and applications for dependent data.Includs methods for the analysis of longitudinal data and corrections for degrees of freedom.Covers growth curve modeling, directional dep作者: 后天習(xí)得 時(shí)間: 2025-3-27 20:45
http://image.papertrans.cn/e/image/284526.jpg作者: RODE 時(shí)間: 2025-3-27 22:52
Time in Latent Growth Curve Modelsn the construction of analytical models. In this chapter, we look at how longitudinal data are analyzed in latent growth curve models. We focus on the real-world problem of sampling-time variation, when individuals do not have exactly equal intervals between measurements, its consequences, and how to handle it.作者: cumulative 時(shí)間: 2025-3-28 04:40
Bootstrap Methods for Robust Multilevel Analysis are nested within classrooms in educational studies, or participants are repeatedly measured at different time points in longitudinal studies. Multilevel models (MLM), also known as hierarchical linear models and mixed effects models, have been widely used to account for clustered data.作者: 放大 時(shí)間: 2025-3-28 06:42
https://doi.org/10.1007/978-3-031-56318-8analysis of longitudinal panel count data; close proximity data; clustered or paired data; corrections 作者: 解決 時(shí)間: 2025-3-28 11:28 作者: Keratin 時(shí)間: 2025-3-28 17:26
Engineered Fe-Based Nanocolumnar Films,n the construction of analytical models. In this chapter, we look at how longitudinal data are analyzed in latent growth curve models. We focus on the real-world problem of sampling-time variation, when individuals do not have exactly equal intervals between measurements, its consequences, and how to handle it.作者: Predigest 時(shí)間: 2025-3-28 20:18
Active Targeting of Nanomedicines are nested within classrooms in educational studies, or participants are repeatedly measured at different time points in longitudinal studies. Multilevel models (MLM), also known as hierarchical linear models and mixed effects models, have been widely used to account for clustered data.作者: Arthr- 時(shí)間: 2025-3-29 02:07 作者: 平靜生活 時(shí)間: 2025-3-29 04:20
Model Selection for Classification,e different methods provide for accommodating the structure and analysis of multilevel data, and we illustrate their application in a series of simulated examples. Finally, we also review the availability of imputation- and model-based methods in statistical software and provide guidance for their application in practice.作者: dainty 時(shí)間: 2025-3-29 09:24 作者: indignant 時(shí)間: 2025-3-29 15:16 作者: invert 時(shí)間: 2025-3-29 17:48 作者: 莎草 時(shí)間: 2025-3-29 22:11 作者: 過份好問 時(shí)間: 2025-3-30 03:21
Electromagnetic Wave Absorption Materials, It turns out that the proposed algorithm determines a drift matrix whose principle directions (eigenvectors) are qualitatively equivalent to the ones estimated via ., but the associated eigenvalues differ substantially, leading to quantitatively different conclusions.作者: 膝蓋 時(shí)間: 2025-3-30 07:56 作者: 大炮 時(shí)間: 2025-3-30 11:02
Continuous Time Modeling in the Social Sciences: History and Philosophical Background arbitrary places. Recent developments discussed include hierarchical Bayesian analysis, time-varying parameters, and mediation analysis. Philosophical issues related to continuous time modeling are discussed, culminating in the question, which was raised already in antiquity, whether change in continuous time is possible at all.作者: GLIB 時(shí)間: 2025-3-30 12:30 作者: Chronological 時(shí)間: 2025-3-30 19:45 作者: Gratulate 時(shí)間: 2025-3-30 22:56
ees of freedom.Covers growth curve modeling, directional dep.This book covers the following subjects: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section d作者: Oration 時(shí)間: 2025-3-31 03:38
High-Energy Particle Accelerationps of countries with respect to the evolution of the contamination rate and show that the median population age is the main predictor of group membership. We do however not see any sign of efficiency of the sanitary measures taken by the different countries against the propagation of the virus.作者: sphincter 時(shí)間: 2025-3-31 06:34
Finite Mixture Models for an Underlying Beta Distribution with an Application to COVID-19 Dataps of countries with respect to the evolution of the contamination rate and show that the median population age is the main predictor of group membership. We do however not see any sign of efficiency of the sanitary measures taken by the different countries against the propagation of the virus.
