scholarly journals Model comparison with composite likelihood information criteria

Bernoulli ◽  
2014 ◽  
Vol 20 (4) ◽  
pp. 1738-1764 ◽  
Author(s):  
Chi Tim Ng ◽  
Harry Joe
Keyword(s):  
Model Comparison ◽  
Information Criteria ◽  
Composite Likelihood

Hysteretic Poisson INGARCH model for integer-valued time series

2017 ◽  
Vol 17 (6) ◽  
pp. 401-422 ◽  
Author(s):  
Buu-Chau Truong ◽  
Cathy WS Chen ◽  
Songsak Sriboonchitta
Keyword(s):  
Time Series ◽  
Model Comparison ◽  
Monte Carlo Sampling ◽  
Information Criteria ◽  
Model Parameters ◽  
Modelling Framework ◽  
South Wales ◽  
Proposed Model ◽  
Over Dispersion ◽  
Ingarch Model

This study proposes a new model for integer-valued time series—the hysteretic Poisson integer-valued generalized autoregressive conditionally heteroskedastic (INGARCH) model—which has an integrated hysteresis zone in the switching mechanism of the conditional expectation. Our modelling framework provides a parsimonious representation of the salient features of integer-valued time series, such as discreteness, over-dispersion, asymmetry and structural change. We adopt Bayesian methods with a Markov chain Monte Carlo sampling scheme to estimate model parameters and utilize the Bayesian information criteria for model comparison. We then apply the proposed model to five real time series of criminal incidents recorded by the New South Wales Police Force in Australia. Simulation results and empirical analysis highlight the better performance of hysteresis in modelling the integer-valued time series.

Composite likelihood model comparison test under fixed and local alternatives

Stat ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. e182
Author(s):  
Yawen Xu ◽  
Xin Gao ◽  
Xiaogang Wang ◽  
Augustine Wong
Keyword(s):  
Model Comparison ◽  
Composite Likelihood ◽  
Local Alternatives ◽  
Comparison Test ◽  
Likelihood Model

scholarly journals Abundance Indices of Skipjack Tuna (Katsuwonus pelamis) from the Indonesian Drifting Gillnet Fishery in the Indian Ocean

2018 ◽  
Author(s):  
Dian Novianto ◽  
Ilham Ilham ◽  
Bram Setyadji ◽  
Chandara Nainggolan ◽  
Djodjo Suwardjo ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Comparison ◽  
Linear Models ◽  
Information Criteria ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Skipjack Tuna ◽  
Abundance Indices ◽  
Marginal Means ◽  
Over Exploitation

Skipjack tuna supports a valuable commercial fishery in Indonesia. Skipjack tuna are exploited in the Indian and Pacific Oceans with a variety of gear but drifting gillnets are a common method used by Indonesian fishers. However, despite of its importance, little information on the drifting gillnet fishery is available. This study describes a preliminary examination of the catch and effort data from the Indonesian skipjack drifting gillnet fishery. Utilizing daily landing report from 2010-2015, nominal catch per unit of effort (CPUE) data were calculated as kg/day at sea. Generalized Linear Models (GLM) were used to standardize the CPUE, using year, quarter, day at sea, and area as fixed variables. Model Goodness-of-fit and model comparison was carried out with the Akaike Information Criteria (AIC), the pseudo coefficient of determination (R2) and model validation with a residual analysis. The final estimation of abundance indices was calculated by least square means (LSMeans) or Marginal Means. The results showed that days accounted for most of the variation in CPUE, followed by year, quarter, and area. In general, there were no noticeable trends indicative of over exploitation or population depletion suggesting a sustainable fishery for Skipjack tuna in Indonesian waters.

scholarly journals Spatial Models for Infants HIV/AIDS Incidence Using an Integrated Nested Laplace Approximation Approach

2019 ◽  
Author(s):  
Susan Nzula Mutua
Keyword(s):  
Random Effects ◽  
Model Comparison ◽  
Well Being ◽  
Laplace Approximation ◽  
Hiv Treatment ◽  
Information Criteria ◽  
Mother To Child Transmission ◽  
Child Transmission ◽  
Integrated Nested Laplace Approximation ◽  
Mother To Child

Abstract Background Kenya has made significant progress in the elimination of mother to child transmission of HIV through increasing access to HIV treatment and improving the health and well-being of women and children living with HIV. Despite this progress, broad geographical inequalities in infant HIV outcomes still exist. This study aimed at assessing the spatial distribution of HIV amongst infants, areas of abnormally high risk and associated risk factors for mother to child transmission of HIV using INLA and SPDE approach. Methods Data were obtained from the Early infant diagnosis (EID) database that is routinely collected for infants under one year for the year 2017. We performed both areal and point-reference analysis. Bayesian hierarchical Poisson models with spatially structured random effects were fitted to the data to examine the effects of the covariates on infant HIV risk. Spatial random effects were modelled using Conditional autoregressive model (CAR) and stochastic partial differential equations (SPDEs). Inference was done using Integrated Nested Laplace Approximation. Posterior probabilities for exceedance were produced to assess areas where the risk exceeds 1. The Deviance Information Criteria (DIC) selection was used for model comparison and selection. Results CAR model outperformed similar competing models in modeling and mapping HIV Relative Risk in Kenya. It had a smaller DIC among the rest (DIC = 306.36)) The SPDE model outperformed the spatial GLM model based on the DIC statistic. Highly active antiretroviral therapy (HAART) and breastfeeding were found to be negatively and positively associated with infant HIV positivity respectively [-0.125, 95% Credible Interval (Cred. Int.)= -0.348, -0.102], [0.178, 95% Cred. Int. -0.051, 0.412].Conclusion The study provides relevant strategic information required to make investment decisions for targeted high impact interventions to reduce HIV infections among infants in Kenya.

scholarly journals Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1693 ◽  
Author(s):  
María Gabriela Pizarro Inostroza ◽  
Francisco Javier Navas González ◽  
Vincenzo Landi ◽  
Jose Manuel León Jurado ◽  
Juan Vicente Delgado Bermejo ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Dry Matter ◽  
Model Comparison ◽  
Minimum Mean Square Error ◽  
Goat Milk ◽  
Individual Performance ◽  
Information Criteria ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
The Individual

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry.

scholarly journals Estimation of nested and zero-inflated ordered probit models

2021 ◽  
Vol 21 (1) ◽  
pp. 3-38
Author(s):  
David Dale ◽  
Andrei Sirchenko
Keyword(s):  
Goodness Of Fit ◽  
Model Comparison ◽  
Probit Model ◽  
Information Criteria ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Finite Sample ◽  
Federal Funds Rate ◽  
Probit Models ◽  
Ordered Probit Models

We introduce three new commands—nop, ziop2, and ziop3—for the estimation of a three-part nested ordered probit model, the two-part zero-inflated ordered probit models of Harris and Zhao (2007, Journal of Econometrics 141: 1073–1099) and Brooks, Harris, and Spencer (2012, Economics Letters 117: 683–686), and a three-part zero-inflated ordered probit model of Sirchenko (2020, Studies in Nonlinear Dynamics and Econometrics 24: 1) for ordinal outcomes, with both exogenous and endogenous switching. The three-part models allow the probabilities of positive, neutral (zero), and negative outcomes to be generated by distinct processes. The zero-inflated models address a preponderance of zeros and allow them to emerge in different latent regimes. We provide postestimation commands to compute probabilistic predictions and various measures of their accuracy, to assess the goodness of fit, and to perform model comparison using the Vuong test (Vuong, 1989, Econometrica 57: 307–333) with the corrections based on the Akaike and Schwarz information criteria. We investigate the finite-sample performance of the maximum likelihood estimators by Monte Carlo simulations, discuss the relations among the models, and illustrate the new commands with an empirical application to the U.S. federal funds rate target.

scholarly journals Spatial pattern analysis of line-segment data in ecology

2021 ◽  
Author(s):  
Luke A. Yates ◽  
Barry W. Brook ◽  
Jessie C. Buettel
Keyword(s):  
Monte Carlo ◽  
Spatial Pattern ◽  
Line Segment ◽  
Model Comparison ◽  
Pattern Analysis ◽  
Information Criteria ◽  
General Nature ◽  
Spatial Pattern Analysis ◽  
List Type ◽  
Segment Data

AbstractThe spatial analysis of linear features (lines and curves) is a challenging and rarely attempted problem in ecology. Existing methods are typically expressed in abstract mathematical formalism, making it difficult to assess their relevance and transferability into an ecological setting. A set of concrete and accessible tools is needed.We develop a new method to analyse the spatial patterning of line-segment data. It is based on a generalisation of Ripley’s K-function and includes an analogue of the transformed L-function, together with estimators and theoretical expectation values. We introduce a class of line-segment processes, related to the Boolean model, which we use in conjunction with Monte-Carlo methods and information criteria to generate and compare candidate models. We demonstrate the utility of our method using fallen tree (dead log) data collected from two one-hectare Australian tall eucalypt forest plots.Comparing six line-segment models, we find for both plots that the distribution of fallen logs is best explained by plot-level spatial heterogeneity. The use of non-uniform distributions to model dead-log orientation on the forest floor improves model performance in one of the two sites. Our case study highlights the challenges of model comparison in spatial-pattern analysis, where Monte-Carlo approaches based on the discrepancy of simulated summary functions can generate a different ranking of models than that of information criteria.These methods are of a general nature and are applicable to any line-segment data. In the context of forest ecology, the integration of fallen logs as linear structural features in a landscape with the point locations of living trees, and a quantification of their interactions, will yield new insights into the functional and structural role of tree fall in forest communities and their enduring post-mortem ecological legacy as spatially distributed decomposing logs.

scholarly journals Model Fit and Comparison in Finite Mixture Models: A Review and a Novel Approach

2021 ◽  
Vol 6 ◽  
Author(s):  
Kevin J. Grimm ◽  
Russell Houpt ◽  
Danielle Rodgers
Keyword(s):  
Mixture Models ◽  
Model Comparison ◽  
Latent Class ◽  
Finite Mixture Models ◽  
Model Simulation ◽  
Information Criteria ◽  
Finite Mixture ◽  
Model Fit ◽  
Future Research ◽  
Novel Approach

One of the greatest challenges in the application of finite mixture models is model comparison. A variety of statistical fit indices exist, including information criteria, approximate likelihood ratio tests, and resampling techniques; however, none of these indices describe the amount of improvement in model fit when a latent class is added to the model. We review these model fit statistics and propose a novel approach, the likelihood increment percentage per parameter (LIPpp), targeting the relative improvement in model fit when a class is added to the model. Simulation work based on two previous simulation studies highlighted the potential for the LIPpp to identify the correct number of classes, and provide context for the magnitude of improvement in model fit. We conclude with recommendations and future research directions.

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