Multinomial Processing Tree Models

2009 ◽  
Vol 217 (3) ◽  
pp. 108-124 ◽  
Author(s):  
Edgar Erdfelder ◽  
Tina-Sarah Auer ◽  
Benjamin E. Hilbig ◽  
André Aßfalg ◽  
Morten Moshagen ◽  
...  
Keyword(s):  
Cognitive Psychology ◽  
Cognitive Processes ◽  
Linear Models ◽  
General Purpose ◽  
Analysis Techniques ◽  
Multinomial Processing Tree ◽  
The Past ◽  
Tree Models ◽  
Recent Trends ◽  
Log Linear

Multinomial processing tree (MPT) models have become popular in cognitive psychology in the past two decades. In contrast to general-purpose data analysis techniques, such as log-linear models or other generalized linear models, MPT models are substantively motivated stochastic models for categorical data. They are best described as tools (a) for measuring the cognitive processes that underlie human behavior in various tasks and (b) for testing the psychological assumptions on which these models are based. The present article provides a review of MPT models and their applications in psychology, focusing on recent trends and developments in the past 10 years. Our review is nontechnical in nature and primarily aims at informing readers about the scope and utility of MPT models in different branches of cognitive psychology.

A Hierarchy of Multinomial Models for Multidimensional Source Monitoring

Methodology ◽  
2005 ◽  
Vol 1 (1) ◽  
pp. 2-17 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Source Monitoring ◽  
Source Information ◽  
Model Comparisons ◽  
Storage And Retrieval ◽  
Multinomial Processing Tree ◽  
Multinomial Models ◽  
Hierarchical Relationship ◽  
Statistical Framework ◽  
Tree Models

Abstract. Several models have been proposed for the measurement of cognitive processes in source monitoring. They are specified within the statistical framework of multinomial processing tree models and differ in their assumptions on the storage and retrieval of multidimensional source information. In the present article, a hierarchical relationship is demonstrated between multinomial models for crossed source information ( Meiser & Bröder, 2002 ), for partial source memory ( Dodson, Holland, & Shimamura, 1998 ) and for several sources ( Batchelder, Hu, & Riefer, 1994 ). The hierarchical relationship allows model comparisons and facilitates the specification of identifiability conditions. Conditions for global identifiability are discussed, and model comparisons are illustrated by reanalyses and by a new experiment on the storage and retrieval of multidimensional source information.

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods

2021 ◽  
pp. 1471082X2110347
Author(s):  
Panagiota Tsamtsakiri ◽  
Dimitris Karlis
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Bayesian Model ◽  
Linear Models ◽  
Simulation Experiment ◽  
Mean Values ◽  
The Past ◽  
Competing Models ◽  
Selection For ◽  
Log Linear

There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.

Abstract MP77: Will the World Health Organization’s “25 x 25” Goal Be Achieved? An Ecologic Time Trend Analysis of Relative and Absolute Premature Mortality Risk Due to Non-Communicable, Chronic Diseases

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Abigail Baldridge ◽  
Jay Pandit ◽  
Mark Huffman
Keyword(s):  
Chronic Diseases ◽  
Mortality Risk ◽  
Linear Models ◽  
Premature Mortality ◽  
World Health ◽  
Population Estimates ◽  
Middle Income ◽  
Middle Income Countries ◽  
Recent Trends ◽  
Log Linear

Objectives: To evaluate country-level time trends (1994-2011) in premature (30-69 years) mortality from non-communicable, chronic diseases (NCDs), including cardiovascular diseases, and to create forward projections to 2025 to evaluate the WHO’s goal of reducing the risk of premature mortality from NCDs by 25% by 2025. Methods: Using publicly available data from the WHO Mortality Database, we created annual estimates of risk of premature (30-69 years) NCD mortality (1994-2011). The sample included data from all countries reporting NCD mortality data from ≥2 years (n=116) and all countries reporting population estimates over the same years (n=135). We matched these datasets by country, year, division, administrative grouping and sex to reach a final sample of 60 countries (193 WHO Member States, 2011). We used ordinary least squares and log-linear Poisson regression models stratified by sex to evaluate the annual change in risk of premature mortality. We then created forward projections through 2025 using log-linear models. We used extrapolated premature mortality risk at 2025 and compared risk to 2010, with projected United Nations age- and sex-specific population estimates, to evaluate trends. Results: Among all included countries, the average (SD) risk of premature mortality from NCDs based on log-linear models in 1994 was 6.8 (4.2) and 3.9 (2.1) per 1,000 persons in men and women, respectively (Table 1). In 2010, men in lower-middle income countries had the highest rates of premature NCD mortality (7.2 [1.8] per 1,000), and women from high-income OECD countries had the lowest rates (2.0 [0.5] per 1,000). If recent trends continue, the median risk of premature mortality from NCDs will decrease by 25.1% (IQR 16.4, 37.0) by 2025. Conclusions: Among included countries, if recent trends in risk of premature death from NCDs continue to 2025, 50% of countries will achieve the WHO’s 25 x 25 goal. However, data are disproportionately missing from low- and middle-income countries, which appear less likely overall to achieve this goal.

Log-Linear Modelling and Spatial Analysis

1985 ◽  
Vol 17 (7) ◽  
pp. 931-951 ◽  
Author(s):  
E Aufhauser ◽  
M M Fischer
Keyword(s):  
Spatial Analysis ◽  
Linear Models ◽  
Generalised Linear Models ◽  
Migration Patterns ◽  
Poisson Sampling ◽  
The Past ◽  
The Social ◽  
The Family ◽  
Conventional Models ◽  
Log Linear

In the past decade the social sciences have seen an upsurge of interest in analysing multidimensional contingency tables using log-linear models. Two broad families of log-linear models may be distinguished: the family of conventional models and the family of unconventional models (that is, quasi-log-linear and hybrid models). In this paper a brief review of such models is presented and some linkage to the class of generalised linear models suggested by Nelder and Wedderburn is provided. The great potential of log-linear models for spatial analysis is illustrated in applying conventional and unconventional models in a migration context to identify intertemporal stability of migration patterns. The problem that the effective units migrating are households rather than individuals is coped with by postulating a compound Poisson sampling scheme.

When and why are log-linear models self-normalizing?

2015 ◽  
Author(s):  
Jacob Andreas ◽  
Dan Klein
Keyword(s):  
Linear Models ◽  
Log Linear

FAKTOR YANG MEMPENGARUHI BEHAVIOR INTENTION UNTUK PENGGUNAAN APLIKASI DOMPET DIGITAL MENGGUNAKAN MODEL UTAUT2

2020 ◽  
Vol 25 (2) ◽  
pp. 111-122
Author(s):  
Aries Andrianto
Keyword(s):  
Sem Analysis ◽  
Electronic Money ◽  
Analysis Techniques ◽  
Behavior Intention ◽  
The Past ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Positive Effect ◽  
Digital Wallet

Based on Bank Indonesia data, electronic money transactions have grown rapidly in the past 10 years. Throughout 2018, the volume of electronic money transactions was 2.92 billion transactions, growing 16,600 times compared to 2009.This study aims to analyze the factors that influence interest in using the LinkAja digital wallet using the UTAUT 2 method. The object of this study is the LinkAja digital wallet user who is domiciled in Jakarta. The independent variables examined in this study were Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, and Habit on Behavior Intention using PLS-SEM analysis techniques. The results of this study indicate that Price Value has a positive effect on Behavior Intention.

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