scholarly journals Geostatistics of extremes

2011 ◽  
Vol 468 (2138) ◽  
pp. 581-608 ◽  
Author(s):  
A. C. Davison ◽  
M. M. Gholamrezaee
Keyword(s):  
Spatial Domain ◽  
Gaussian Random Fields ◽  
Information Criteria ◽  
Composite Likelihood ◽  
Model Fit ◽  
Likelihood Inference ◽  
Stable Model ◽  
Competing Models ◽  
Maximum Temperatures ◽  
Occurrence Times

We describe a prototype approach to flexible modelling for maxima observed at sites in a spatial domain, based on fitting of max-stable processes derived from underlying Gaussian random fields. The models we propose have generalized extreme-value marginal distributions throughout the spatial domain, consistent with statistical theory for maxima in simpler cases, and can incorporate both geostatistical correlation functions and random set components. Parameter estimation and fitting are performed through composite likelihood inference applied to observations from pairs of sites, with occurrence times of maxima taken into account if desired, and competing models are compared using appropriate information criteria. Diagnostics for lack of model fit are based on maxima from groups of sites. The approach is illustrated using annual maximum temperatures in Switzerland, with risk analysis proposed using simulations from the fitted max-stable model. Drawbacks and possible developments of the approach are discussed.

scholarly journals Nuclear data adjustment using Bayesian inference, diagnostics for model fit and influence of model parameters

2020 ◽  
Vol 239 ◽  
pp. 13003
Author(s):  
D. Kumar ◽  
S. B. Alam ◽  
H. Sjöstrand ◽  
J.M. Palau ◽  
C. De Saint Jean
Keyword(s):  
Bayesian Inference ◽  
Degrees Of Freedom ◽  
Nuclear Data ◽  
Information Criteria ◽  
Least Square ◽  
Model Fit ◽  
Adjustment Process ◽  
Model Parameters ◽  
Test Case ◽  
Bayesian Approximation

The mathematical models used for nuclear data evaluations contain a large number of theoretical parameters that are usually uncertain. These parameters can be calibrated (or improved) by the information collected from integral/differential experiments. The Bayesian inference technique is used to utilize measurements for data assimilation. The Bayesian approximation is based on the least-square or Monte-Carlo approaches. In this process, the model parameters are optimized. In the adjustment process, it is essential to include the analysis related to the influence of model parameters on the adjusted data. In this work, some statistical indicators such as the concept of Cook’s distance; Akaike, Bayesian and deviance information criteria; effective degrees of freedom are developed within the CONRAD platform. Further, these indicators are applied to a test case of 155Gd to evaluate and compare the influence of resonance parameters.

scholarly journals Composite Likelihood Inference in a Discrete Latent Variable Model for Two-Way “Clustering-by-Segmentation” Problems

2017 ◽  
Vol 26 (2) ◽  
pp. 388-402 ◽  
Author(s):  
Francesco Bartolucci ◽  
Francesca Chiaromonte ◽  
Prabhani Kuruppumullage Don ◽  
Bruce G. Lindsay
Keyword(s):  
Latent Variable ◽  
Composite Likelihood ◽  
Likelihood Inference ◽  
Latent Variable Model ◽  
Variable Model

A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses

Psychometrika ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. 425-441 ◽  
Author(s):  
Vassilis G. S. Vasdekis ◽  
Silvia Cagnone ◽  
Irini Moustaki
Keyword(s):  
Latent Variable ◽  
Latent Variable Models ◽  
Composite Likelihood ◽  
Likelihood Inference

scholarly journals Assessment, Testing and Comparison of Statistical Models using R

2021 ◽  
Author(s):  
Daniel Lüdecke ◽  
Mattan S. Ben-Shachar ◽  
Indrajeet Patil ◽  
Philip Waggoner ◽  
Dominique Makowski
Keyword(s):  
Regression Models ◽  
Model Fit ◽  
Fit Indices ◽  
Diagnostic Plots ◽  
Fit Statistics ◽  
Best Fitting ◽  
Competing Models ◽  
Crucial Part ◽  
Fitting Model ◽  
Consistent Approach

A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many competing models. Upon investigation, fit indices should also be reported both visually and numerically to bring readers in on the investigative effort. While functions to build and produce diagnostic plots or to compute fit statistics exist, these are located across many packages, which results in a lack of a unique and consistent approach to assess the performance of many types of models. The result is a difficult-to-navigate, unorganized ecosystem of individual packages with different syntax, making it onerous for researchers to locate and use fit indices relevant for their unique purposes. The performance package in R fills this gap by offering researchers a suite of intuitive functions with consistent syntax for computing, building, and presenting regression model fit statistics and visualizations.

Confirmatory Factor Analysis of the Beck Anxiety Inventory Among Chinese Postgraduates

2018 ◽  
Vol 46 (8) ◽  
pp. 1245-1254
Author(s):  
Yicheng Zhou ◽  
Jing An ◽  
Mingwang Cheng ◽  
Liying Sheng ◽  
Guoqiang Rui ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Structure ◽  
Reliability And Validity ◽  
Model Fit ◽  
Beck Anxiety Inventory ◽  
Confirmatory Factor ◽  
Competing Models ◽  
Potential Factor ◽  
Analysis Models

We examined the factor structure of the Beck Anxiety Inventory (BAI) with 531 students at 6 universities in Nanjing to evaluate its applicability as a measure of the anxiety of Chinese postgraduates. We performed exploratory factor analysis to identify the potential factor structure of the BAI. We referred to confirmatory factor analysis models from previous studies for model fit. All 7 competing models fitted well with the students' data. The 4-factor structure proposed by Wetherell and Areán yielded the best fit. Results indicate that the BAI has satisfactory reliability and validity among Chinese postgraduates.

scholarly journals Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Harry Joe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Probability Generating Function ◽  
Likelihood Estimation ◽  
Model Fit ◽  
Likelihood Inference ◽  
Series Data ◽  
Time Series Models ◽  
Autoregressive Time Series ◽  
The Past

For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.

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