Models and likelihood models statistical. Medical search. Frequent questions

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. (routledge.com)
After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. (routledge.com)
Emphasizing the principles of Bayesian inference and Bayesian model comparison, this book develops a unique methodology for solving challenging inference problems. (routledge.com)
This interesting book on model selection provides a nice review of the frequentist, likelihood, and Bayesian approaches to inference and model comparison. (routledge.com)
This is a stimulating book that should be of interest to Bayesians and statisticians with a general interest in statistical inference. (routledge.com)
This course provides you with these skills by giving an introduction to the principles of statistical inference and linear statistical models using the freely available statistical package R. (edu.au)
1. Ability to derive the distributional results needed for statistical inference. (edu.au)
Theory and inference for a class of observation-driven models with application to time series of counts. (uni-muenchen.de)
Inference and testing for structural change in general Poisson autoregressive models. (uni-muenchen.de)
This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. (repec.org)
Bayesian Inference in Econometric Models Using Monte Carlo Integration ," Econometrica , Econometric Society, vol. 57(6), pages 1317-1339, November. (repec.org)
Fallacy of data-selective inference in modelling networks. (warwick.ac.uk)
For statistical inference, longitudinal data are available from the UK Medical Research Council Cognitive Function and Ageing Study. (mpg.de)
Maximum likelihood is used for statistical inference. (mpg.de)
Statistical Inference on Residual Life: Inference on Mean Residual Life-Overview Jeong, Jong-Hyeon 2013-12-14 00:00:00 [Statistical inference based on the remaining lifetimes would be intuitively more appealing than the popular hazard function defined as the risk of immediate failure, whose interpretation could be sometimes difficult to be grasped. (deepdyve.com)
This chapter presents a brief overview of statistical inference on the mean residual life. (deepdyve.com)
Statistical Inference on Residual Life Inference on Mean Residual Life-Overview %22&body=%0AI%20found%20an%20article%20you%20might%20be%20interested%20in. (deepdyve.com)
In this paper, the semiparametric inference procedures are studied with an empirical likelihood ratio method. (bepress.com)
Secondly, the approximate technique is a generalization of likelihood weighting in Bayesian Networks, and allows us to perform sampling-based inference with lower rejection rate and variance. (easychair.org)
The inference problem for diffusion processes is generally difficult due to the lack of closed form expressions for the likelihood function. (lu.se)
It is a broad and interdisciplinary tool combining mathematics, computer intensive methods, statistical inference and applied probability. (lu.se)
Statistical inference for complex systems using computer intensive Monte Carlo methods, such as sequential Monte Carlo, Markov chains Monte Carlo and likelihood-free methods for Bayesian inference. (lu.se)
Statistical inference for stochastic differential equation models and Lévy processes with applications in biology. (lu.se)

The likelihood function (often simply called the likelihood) is the joint probability (or probability density) of observed data viewed as a function of the parameters of a statistical model. (wikipedia.org)
Meanwhile in Bayesian statistics, parameter estimates are derived from the converse of the likelihood, the so-called posterior probability, which is calculated via Bayes' rule. (wikipedia.org)
The likelihood function, parameterized by a (possibly multivariate) parameter θ {\displaystyle \theta } , is usually defined differently for discrete and continuous probability distributions (a more general definition is discussed below). (wikipedia.org)
Given a probability density or mass function x ↦ f ( x ∣ θ ) , {\displaystyle x\mapsto f(x\mid \theta ),} where x {\displaystyle x} is a realization of the random variable X {\displaystyle X} , the likelihood function is θ ↦ f ( x ∣ θ ) , {\displaystyle \theta \mapsto f(x\mid \theta ),} often written L ( θ ∣ x ) . {\displaystyle {\mathcal {L}}(\theta \mid x). (wikipedia.org)
In other words, when f ( x ∣ θ ) {\displaystyle f(x\mid \theta )} is viewed as a function of x {\displaystyle x} with θ {\displaystyle \theta } fixed, it is a probability density function, and when viewed as a function of θ {\displaystyle \theta } with x {\displaystyle x} fixed, it is a likelihood function. (wikipedia.org)
In recent years, the developments of new families of probability distributions have received greater attention as a result of desirable properties they exhibit in the modelling of data sets. (hindawi.com)
To this end, the need for the use of appropriate and very efficient probability distributions in the modelling of medical data is fundamentally important. (hindawi.com)
This is based on the intuitive idea that the best values of the parameters are those that maximize the probability of the observed data given the model. (nasa.gov)
The likelihood is defined as the total probability of observing the data given the model and current parameters. (nasa.gov)
Using the climate models, the authors found at least a 6% probability that summer sea ice in the Arctic Ocean will disappear with warming of 1.5 degrees above pre-industrial levels. (phys.org)
SCORE is the resulting probability value assignment resulting from your model. (tamu.edu)

This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t -tests and other standard statistical methods for hypothesis testing. (routledge.com)
The author then thoroughly discusses the use of the multinomial model and noninformative Dirichlet priors in "model-free" or nonparametric Bayesian survey analysis, before covering normal regression and analysis of variance. (routledge.com)
Bayesian Linear Model: Gory Details. (rdrr.io)

In maximum likelihood estimation, the arg max (over the parameter θ {\displaystyle \theta } ) of the likelihood function serves as a point estimate for θ {\displaystyle \theta } , while the Fisher information (often approximated by the likelihood's Hessian matrix) indicates the estimate's precision. (wikipedia.org)
maximum likelihood methods for estimation and testing, and goodness-of-fit tests. (edu.au)
4. Demonstrate understanding of the theory of maximum likelihood estimation for a scalar parameter. (edu.au)
The first is parameter estimation, which comprises finding the parameters for a given model that provide the best fit to the data and then estimating uncertainties on these parameters. (nasa.gov)
The standard statistic used in parameter estimation is the maximum likelihood. (nasa.gov)
Estimators combine two modeling approaches to reduce bias and improve the accuracy of causal effect estimation. (mmaglobal.com)
However, parameter estimation is hard because of intrinsic identifiability issues and intractable likelihood functions. (jstatsoft.org)
Both aforementioned papers discuss similar expectation-maximization-like algorithms as their pseudo maximum likelihood estimation procedure. (jstatsoft.org)
Filho, G.L., Rezende, A.C.B., Cruz, L.F., Vieira, F.H.T. and Lemos, R.P. (2020) 2-D DOA Estimation in a Cuboid Array Based on Metaheuristic Algorithms and Maximum Likelihood. (scirp.org)
The authors in [3] [4] present an approach for DOA estimation based on the maximum likelihood estimator. (scirp.org)
The first two methods involved an imputation procedure and the third method used maximum likelihood estimation (MLE). (cdc.gov)
check the prerequisites of the model before modelling multivariate data, · evaluate the plausibility of a performed study, · reflect over the limitations of the chosen model and estimation method, as well as alternative solutions. (lu.se)

To update the British growth reference, anthropometric data for weight, height, body mass index (weight/height2) and head circumference from 17 distinct surveys representative of England, Scotland and Wales (37,700 children, age range 23 weeks gestation to 23 years) were analysed by maximum penalized likelihood using the LMS method. (nih.gov)
Two negative binomial quasi-maximum likelihood estimates (NB-QMLE's) for a general class of count time series models are proposed. (uni-muenchen.de)
Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator. (uni-muenchen.de)
For macromolecular crystallography, maximum-likelihood functions are required in order to account for the large model errors that are present during phasing. (iucr.org)
Maximum likelihood from incomplete data via the EM algorithm. (scirp.org)
This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. (scirp.org)
We present statistical inferences using an analytical method based on empirical precision matrix as well as the maximum likelihood method. (lu.se)

A 35 , 517-525], wherever likelihood target functions are required. (iucr.org)
Compared with the model errors, the relatively smaller data errors have not been the focus of the development of macromolecular likelihood functions, but recent advances have raised the importance of dealing properly with both large model and large data errors. (iucr.org)
In the multivariate adjusted models, after controlling for age, education, marital status, and BMI, we found a positive association between NH 3 and prisoners, but not blood H 2 S. While the functions of the two toxicants were quite different, blood NH 3 may be a potential biological risk factor for behavioral disorders and blood H 2 S showed neuroprotection. (hindawi.com)

This course provides an introduction to parameter mixture distributions, conditional independence and asymptotic models used to construct multivariate models in higher dimensions, along with a discussion of why there is a need to separate the dependence structure from the marginal distributions. (lu.se)
describe how to define copulas and their relationship to multivariate extreme value distributions, · describe different measures of association for multivariate data, · explain which statistical methods can be used for the analysis of copulas and extreme values. (lu.se)
process multivariate data for analysis of copulas and extreme values, · fit copulas and multivariate extreme value distributions using different methods, · validate the fitted models and make suitable modifications of the models, · use the resulting models for prediction, · use a statistical computer program for analysis of data, · present the analysis and conclusions of a practical problem in a written report. (lu.se)
Methods for constructing multivariate models in higher dimensions: copula representations, Sklar's theorem and the Fréchet-Hoeffding bounds for joint distributions. (lu.se)
Abstract: Spatial multivariate econometric models are typically used to model dependences between locations, often by adapting univariate models such as SEM or SLM models. (lu.se)
Our new multivariate spatial econometric model can account for feedback effects between different variables within the same location. (lu.se)
We discussed model identification of both the previously used multivariate model and our extended version. (lu.se)

Machine learning algorithms, more precisely the logistic regression algorithm , can help predict the likelihood of events by looking at historical data points. (g2.com)

These have been addressed with the formulation of LLGI, a log-likelihood-gain function in terms of the Bragg intensities and their associated experimental error estimates. (iucr.org)
By using both models, doubly robust estimators provide more consistent estimates of the causal effect even if either one of the models is mis-specified. (mmaglobal.com)
The model provided accurate estimates of yellow starthistle incidence within each specified land-use category and performed well in subsequent statistical validations. (bioone.org)
Modelling a future forecast that estimates the regular number of confirmed cases enhances the implementation of rules aimed at controlling the spread of COVID-19. (who.int)

The models can be nonlinear and/or non-normal. (repec.org)
A nonlinear prediction model has been developed using a polar coordinate transformation of landscape characteristics to predict the likelihood of yellow starthistle occurrence in north-central Idaho. (bioone.org)
Sequential Monte Carlo methods (SMC, a.k.a. particle filters) have revolutionized and simplified the problem of filtering for nonlinear, non-Gaussian models. (lu.se)

The uncertainty in the background modeling is assumed to be Gaussian. (nasa.gov)
This case is more difficult than that of Gaussian data because the difference between two Poisson variables is not another Poisson variable so the background data cannot be subtracted from the source and used within the C statistic. (nasa.gov)
Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian mixture copula models (GMCM) for general unsupervised learning based on clustering. (jstatsoft.org)

Several chapters also introduce statistical methods and procedures to allow readers to analyze behavioral data. (peterlang.com)
5. Ability to analyse data and fit linear regression models using R. (edu.au)
Econometric models based on count data: Comparisons and applications of some estimators and tests. (uni-muenchen.de)
Modelling time series of count data. (uni-muenchen.de)
Dynamic Equilibrium Economies: A Framework for Comparing Models and Data ," Review of Economic Studies , Oxford University Press, vol. 65(3), pages 433-451. (repec.org)
Dynamic Equilibrium Economies: A Framework for Comparing Models and Data ," NBER Technical Working Papers 0174, National Bureau of Economic Research, Inc. (repec.org)
Dynamic equilibrium economies: a framework for comparing models and data ," Staff Report 243, Federal Reserve Bank of Minneapolis. (repec.org)
Dynamic equilibrium economies: a framework for comparing models and data ," Finance and Economics Discussion Series 1997-23, Board of Governors of the Federal Reserve System (U.S. (repec.org)
Dynamic equilibrium economies: a framework for comparing models and data ," Working Papers 97-7, Federal Reserve Bank of Philadelphia. (repec.org)
A suite of code for covariance modelling in longitudinal data, including an implementation of the method in Zhang, Leng, and Tang (JRSSB, 2015), can be found here . (warwick.ac.uk)
Discrete longitudinal data modeling with a mean-correlation regression approach. (warwick.ac.uk)
The applications of the developed family to real data sets in medical studies revealed that the special distribution (Harmonic mixture Weibul Weibull distribution) provided a better fit to the data sets than other competitive models. (hindawi.com)
A location-scale regression model was developed from the family and its application demonstrated using survival time data of hypertensive patients. (hindawi.com)
The efficient modelling of medical data is useful in providing good understanding of the distribution of disease incidence and prevalence in medical studies. (hindawi.com)
In medical and biological studies, the Weibull distribution among numerous classical distributions is a widely applied model for analyzing data with monotone hazard rate shapes. (hindawi.com)
For complex biological phenotypic traits with nonmonotone hazard rate shapes, the Weibull distribution does not have the flexibility to model such data. (hindawi.com)
The second operation is testing whether the model and its best-fit parameters actually match the data. (nasa.gov)
the values of the predicted data rates based on the model (with current parameters) and instrumental response. (nasa.gov)
The previous section assumed that the only contribution to the observed data was from the model. (nasa.gov)
If the statistic is specified as cstatN where N is an integer then the same formula is used except that the data and model are binned so that there are at least N counts in each bin. (nasa.gov)
Objective: The aim is statistical analysis of longitudinal multi-state data taking into account missing data and potential misclassification of state. (mpg.de)
Conclusions: Statistical analysis of longitudinal multi-state data can take into account missing data and potential misclassification of state. (mpg.de)
Proportional mean residual life model for right-censored length-biased data. (deepdyve.com)
Statistical method used for estimating treatment effects in observational studies or complex data settings. (mmaglobal.com)
Linear regression and generalised structural equation models were used to examine the data. (springer.com)
We find that a fixation dependent model provides a determining moral choice by modelling it as a fixation better fit and can capture many features of the empirical data. (lu.se)
The first proportional hazard model, introduced by Cox in 1972, works with uncensored data and right censored data. (xlstat.com)
The purpose of the proportional hazard model with interval censored data is, therefore, the same as for the Cox model, but it will also be possible to model survival times for interval-censored data, uncensored data, left censored data or right censored data. (xlstat.com)
If the data contains only uncensored or right-censored observations, it is possible, with this function, to reproduce the results of a Cox model. (xlstat.com)
Therefore, the team combined their observations with model calculations to find the most likely range of atmospheric properties matching the data. (mpg.de)
The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box-Ljung test. (who.int)
Model validation for a data set is also discussed. (lu.se)
Spatio-temporal stochastic modelling with applications in extreme value analysis, fatigue and risk analysis, and analysis of environment, climate and oceanographic data. (lu.se)
Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. (bvsalud.org)

I like this book very much … a worthy new tool based on the posterior distribution of the likelihood with good examples of its capabilities and limitations. (routledge.com)

marginalLikelihood_bySufficientStatistics.GaussianNIG: Marginal likelihood of a 'GaussianNIG' object, using. (rdrr.io)

SMC can also be used to construct an approximation to the likelihood function for the parameters of the state-space model of interest. (lu.se)

R Code and an Example for sparse matrix graphical models in Leng and Tang (JASA, 2012). (warwick.ac.uk)

Probabilistic Logic Programs (PLPs) generalize traditional logic programs and allow the encoding of models combining logical structure and uncertainty. (easychair.org)
Stochastic modelling is the science of the mathematical representation of processes and systems evolving randomly, the study of their probabilistic structure and the statistical analysis of unknown features in the models. (lu.se)

Estimating Macroeconomic Models: A Likelihood Approach ," NBER Technical Working Papers 0321, National Bureau of Economic Research, Inc. (repec.org)
Estimating Macroeconomic Models: A Likelihood Approach ," Review of Economic Studies , Oxford University Press, vol. 74(4), pages 1059-1087. (repec.org)
Estimating Macroeconomic Models: A Likelihood Approach ," CEPR Discussion Papers 5513, C.E.P.R. Discussion Papers. (repec.org)
Estimating Macroeconomic Models: A Likelihood Approach ," Levine's Bibliography 122247000000000849, UCLA Department of Economics. (repec.org)
While we only tested the new approach on climate models , we are eager to see if the technique can be applied to other fields, such as stock market predictions, plane accident investigations, or in medical research," says Roman Olson, the lead author and researcher at the Institute for Basic Science in South Korea. (phys.org)
To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. (who.int)

The proposed NB-QMLE's are compared with the Poisson QMLE asymptotically and in finite samples for various well-known particular classes of count time series models such as the (Poisson and negative binomial) Integer GARCH model and the INAR(1) model. (uni-muenchen.de)
Poisson qmle of count time series models. (uni-muenchen.de)
Poisson-geometric INAR(1) process for modeling count time series with overdispersion. (uni-muenchen.de)

Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. (who.int)
Katoch and Sidhu (2021) predicted the spread and the final size of the COVID-19 epidemic in India using the ARIMA model. (who.int)

We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility. (repec.org)
This PhD-level course will present an overview of modern inferential methods for partially observed stochastic processes, with emphasis on state-space models (also known as Hidden Markov Models). (lu.se)

The model structure and prior parameters are stored in a "GaussianInvWishart" object. (rdrr.io)
We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. (repec.org)
An empirical likelihood confidence region is constructed for the regression parameters. (bepress.com)
An old idea, going back at least half a century, is to treat the model parameters as latent processes themselves. (lu.se)

citation needed] any non-trivial event will have a lower likelihood. (wikipedia.org)
Discussion of 'Statistical modelling of citation exchange between statistics journals' by Varin, Cattelan and Firth, Journal of the Royal Statistical Society Series A, 179, 54. (warwick.ac.uk)

R Code (rar file) and an Example for penalised empirical likelihood in Tang and Leng (Biometrika, 2010) and Leng and Tang (Biometrika, 2012). (warwick.ac.uk)

2012), and has been found to and instead give an account based on the distinction be able to predict a number of relationships between gaze between model-based and model-free systems from and choice. (lu.se)
Logistic regression is a statistical method used to predict the outcome of a dependent variable based on previous observations. (g2.com)

R. Olson et al, A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models, Nature Communications (2019). (phys.org)

A set of methods to implement Generalized Method of Moments and Maximal Likelihood methods for Random Utility Models. (r-project.org)

The text concludes with new goodness-of-fit methods for assessing parametric models and a discussion of two-level variance component models and finite mixtures. (routledge.com)
This article reviews flexible statistical methods that are useful for characterizing the effect of potential prognostic factors on disease endpoints. (nih.gov)
Methods for clustering in unsupervised learning are an important part of the statistical toolbox in numerous scientific disciplines. (jstatsoft.org)
Three statistical methods were employed to compare the two analytical methods. (cdc.gov)
1. To discuss fundamental and flexible methods for modern dependence modelling with copulas and to demonstrate how the theory can be used in real life applications. (lu.se)

These exogenous variables may be used to develop prediction models to estimate the potential for yellow starthistle invasion into new areas. (bioone.org)
This prediction model can assist land managers in focusing their efforts by identifying specific areas for survey. (bioone.org)

3. Demonstrate understanding that hypothesis tests, regression and analysis of variance can be seen as part of the same statistical theory of linear models. (edu.au)

A: Homeowner refers to a "verified" homeowner whereas probable homeowner is a household that is "unverified" but scores highly on homeownership predictive model. (tamu.edu)
Probable Multi-Buyer is a member that has no verified purchases but scores highly on mail-order buyer predictive model. (tamu.edu)
If you're wondering what regression analysis is, it's a type of predictive modeling technique used to find the relationship between a dependent variable and one or more independent variables. (g2.com)

The researchers applied the new statistical method to climate model projections of the 21st century. (phys.org)
However, it is recommended to use Cox's proportional hazards model as it provides a more suitable method for this type of case. (xlstat.com)
Each statistical method differed in how the samples that had results below the limit of detection (LOD) were treated. (cdc.gov)
A fourth statistical method that modeled each lab separately using MLE was used for comparison. (cdc.gov)

Statistical Methodology, 31, 73-90. (uni-muenchen.de)

Regression models for the transition intensities are formulated to incorporate covariate information. (mpg.de)

Analysis of the complex problems arising in practice requires an understanding of fundamental statistical principles together with knowledge of how to use suitable modelling techniques. (edu.au)

General state space models are defined in terms of a latent Markov process, from which partial observations can be obtained. (lu.se)

Figure 1 presents a generalized linear statistical model, represented by the line equation Y = 0.002x + 0.067. (bvsalud.org)

2020) predicted the daily confirmed COVID-19 cases for Malaysia using the ARIMA model. (who.int)

A two-stage fitting procedure was developed to model the age trends in median weight and height, and simulation was used to estimate confidence intervals for the fitted centiles. (nih.gov)

En este estudio longitudinal, analizamos los efectos satisfacción laboral. (bvsalud.org)

Applications to survival models and binary outcome models are illustrated. (nih.gov)
Use the interval-censored proportional hazard model to model survival time based on quantitative or qualitative explanatory variables. (xlstat.com)
The principle of the proportional hazards model is to link the survival time of an individual to covariates. (xlstat.com)

The parameter estimate, corresponding standard deviation, Wald's Chi 2 , the corresponding p-value and the confidence interval are displayed for each variable of the model. (xlstat.com)

and to develop a location-scale regression model for studying the relationship between a response variable and a set of covariates. (hindawi.com)
The proportional mean residual life model by Oakes and Dasu (1990) is a regression tool to study the association between life expectancy and its associated covariates. (bepress.com)
This table displays a series of statistics for the independent model (corresponding to the case where there is no impact of covariates, beta=0) and for the adjusted model. (xlstat.com)

While broad theoretical models have been enough has been integrated to pass a decision-threshold. (lu.se)
The course covers theoretical models for the structure and reactions of atomic nuclear, as well as experiments in nuclear physics and their scientific applications. (lu.se)

The statistical properties were comprehensively presented and five special distributions developed from the family. (hindawi.com)

Specifically, with "complex" we mean models for which we are unable to make use of the likelihood function (because it is analytically unavailable or computationally too expensive to evaluate). (lu.se)

2004) and these models have indeed been fruitful for Rangel, 2008), Krajbich and collaborators have proposed an generating new questions and directions, they are notably extension of diffusion models to incorporate visual fixations lacking in their computational specificity. (lu.se)

Our work provides a new statistical and mathematical framework to calculate climate change and impact probabilities," said Jason Evans, a professor who works at the University of New South Wales and its Climate Change Research Centre. (phys.org)

Other fundamental properties of the new model are established. (lu.se)

6. Demonstrate skills in interpreting and communicating the results of statistical analysis, orally and in writing. (edu.au)

Computing using high-level software is also an essential element of modern statistical practice. (edu.au)
In practice, the statistic used is twice the negative log likelihood. (nasa.gov)

suggested to account for many of the findings in the Building on observations that visual fixations play a role in literature (most notably dual-process models, e.g. (lu.se)

Journal of the American Statistical Association, 98, 214-223. (uni-muenchen.de)
Measures of Fit for Calibrated Models ," Journal of Political Economy , University of Chicago Press, vol. 101(6), pages 1011-1041, December. (repec.org)
Journal of the Royal Statistical Society, Series B, to appear. (warwick.ac.uk)
Journal of the Royal Statistical Society Series B, 81, 385-408. (warwick.ac.uk)
Journal of the American Statistical Association, 114, 857-868. (warwick.ac.uk)
Journal of the American Statistical Association, 111, 1196-1207. (warwick.ac.uk)
Journal of the Royal Statistical Society Series B, 78, 589-611. (warwick.ac.uk)
Journal of the Royal Statistical Society. (scirp.org)
Journal of Statistical Software , 70 (2), 1-23. (jstatsoft.org)

Analytical, Diagnostic and Therapeutic Techniques and Equipment5

Phenomena and Processes5

Information Science3

Health Care4

Inference23

Probability11

Bayesian3

Estimation12

Maximum7

Functions3

Multivariate7

Algorithms1

Estimates4

Nonlinear3

Gaussian3

Data34

Posterior distribution1

MarginalLikelihood1

Approximation1

Graphical models1

Probabilistic2

Approach6

Poisson3

20212

Stochastic2

Parameters4

Citation2

Empirical1

Predict2

20191

Random Utility Models1

Methods5

Prediction2

Theory1

Predictive3

Method4

Methodology1

Regression models1

Suitable1

Markov1

Linear statistical1

20201

Procedure1

Longitudinal1

Survival3

Estimate1

Covariates3

Theoretical models2

Distributions1

Evaluate1

Incorporate1

Mathematical1

Fundamental1

Demonstrate1

Practice2

Observations1

Journal9