Results 21 to 30 of about 1,934,057 (295)
Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks
Journal of Agricultural and Resource Economics, 2004 Little research has been conducted on evaluating out-of-sample forecasts of discrete dependent variables. This study describes the large and small sample properties of two forecast evaluation techniques for discrete dependent variables: receiver-operator
F. Bailey Norwood +2 more
doaj +1 more source
Convex and non-convex regularization methods for spatial point processes intensity estimation [PDF]
, 2017 This paper deals with feature selection procedures for spatial point processes intensity estimation. We consider regularized versions of estimating equations based on Campbell theorem derived from two classical functions: Poisson likelihood and logistic ...
Choiruddin, Achmad +2 more
core +4 more sources
Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models [PDF]
Journal of Sciences, Islamic Republic of IranWhen discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. However, the maximum likelihood methods are plagued with substantial calculations for large data sets ...
Mohsen Mohammadzadeh, Leyla Salehi
doaj +1 more source
New developments in the ROOT fitting classes [PDF]
EPJ Web of Conferences, 2019 The ROOT Mathematical and Statistical libraries have been recently improved both to increase their performance and to facilitate the modelling of parametric functions that can be used for performing maximum likelihood fits to data sets to estimate ...
Valls Xavier +3 more
doaj +1 more source
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods [PDF]
Spatial Statistics, 62:100848, 2024, 2023 In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to real-world data because the likelihood function might be slow to evaluate or wholly intractable.
arxiv +1 more source
, 2020 We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in one-dimensional marginal distributions of shear two-point correlation ...
Eifler, Tim +6 more
core +1 more source
Composite Likelihood Inference by Nonparametric Saddlepoint Tests [PDF]
, 2013 The class of composite likelihood functions provides a flexible and powerful toolkit to carry out approximate inference for complex statistical models when the full likelihood is either impossible to specify or unfeasible to compute.
Lunardon, Nicola, Ronchetti, Elvezio
core +3 more sources
The Analysis of Ranking Data Using Score Functions and Penalized Likelihood
Austrian Journal of Statistics, 2017 In this paper, we consider different score functions in order summarize certain characteristics for one and two sample ranking data sets. Our approach is flexible and is based on embedding the nonparametric problem in a parametric framework.
Mayer Alvo, Hang Xu
doaj +1 more source
Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models [PDF]
, 2020 Penalization of the likelihood by Jeffreys' invariant prior, or by a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial generalized linear models.
Firth, David, Kosmidis, Ioannis
core +2 more sources
Spatially multi-scale dynamic factor modeling via sparse estimation
International Journal of Mathematics for Industry, 2019 In many spatio-temporal data, their spatial variations have inherent global and local structures. The spatially continuous dynamic factor model (SCDFM) decomposes the spatio-temporal data into a small number of spatial and temporal variations, where the ...
Takamitsu Araki, Shotaro Akaho
doaj +1 more source