Statistical Modelling

Statistical model

Statistical model validation

Statistical model specification

Statistical Modelling Society

Testing in binary response index models

Exponential family random graph models

Graphical model

First-hitting-time model

Proportional hazards model

Mixed model

Hurdle model

IBM alignment models

Shortcut model

Hubbard model

Phase-field models on graphs

Configuration model

Kinetic exchange models of markets

Bayesian model reduction

Generalized additive model

Structural equation modeling

Multilevel model

Ziff-Gulari-Barshad model

Maximum-entropy random graph model

Ising model

STAR model

Optimality model

Thurstonian model

Abelian sandpile model

Semiparametric model

ARIMA5

• We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. (who.int)
• 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)
• Katoch and Sidhu (2021) predicted the spread and the final size of the COVID-19 epidemic in India using the ARIMA model. (who.int)
• 2020) predicted the daily confirmed COVID-19 cases for Malaysia using the ARIMA model. (who.int)
• Monthly data sets from 2000 to 2016 were used to construct autoregressive integrated moving average (ARIMA) models to predict dengue incidence for 12 provinces across the study region. (who.int)

Predict7

• Statistical relations that predict the track of the storm based on the large-scale conditions are then used to forecast the storm's future position. (britannica.com)
• Our goals are modeling the seasonal dynamics to generate reasonable predictions, and building a model using MODIS data to predict the CO2 flux data at any location. (uwo.ca)
• Doctors can now use a statistical model to predict the best time to do the transplant. (medlineplus.gov)
• 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 final models were able to predict dengue incidence from January to December 2016 that concurred with the observation that dengue epidemics occur mostly in rainy seasons. (who.int)
• A range of approaches, including statistical modelling, mathematical modelling and spatial analysis, have been applied to demonstrate relationships between dengue and climate variables and to predict dengue cases and outbreaks. (who.int)
• 12,13 Statistical models that are commonly constructed to predict dengue incidence cannot precisely predict the time and place of a dengue outbreak. (who.int)

Spatial4

• For our spatial study, we study three models. (uwo.ca)
• In the first model, we decompose the CO2 flux data into different components and build a model based on the spatial correlations of each component. (uwo.ca)
• Among the three spatial models, the GAM performed best in that it had the lowest out of sample prediction mean square error. (uwo.ca)
• The RST uses 2 forms of empirical Bayesian modeling (nonspatial and spatial) to estimate age-standardized rates and 95% credible intervals for user-specified geographic units. (cdc.gov)

Epidemiology1

• Statistical models in epidemiology / David Clayton and Michael Hills. (who.int)

Mathematical3

• Since the 20th century, there have been many exciting achievements in the field of ecology and evolution, many of which were formulated using statistical and mathematical functions. (novapublishers.com)
• In particular, statistical and mathematical models are a necessity for developing some sub-disciplines and theories like population genetics and ecology, neutral theory of molecular evolution and biodiversity, and machine-learning techniques for species distribution modeling. (novapublishers.com)
• This book serves as an elementary guide to showcase some statistical and mathematical models that have been applied and used in contemporary ecological or evolutionary research. (novapublishers.com)

Inference4

• Identifiability is a prerequisite for statistical inference, such as parameter estimation and hypothesis testing. (columbia.edu)
• iii) dynamically assigning scores and attend to the observed units to derive robust features.By integrating the ideas from representation learning, variational Bayes, causal inference, and contrastive training, this dissertation builds tools for risk modeling frameworks that are robust to various peculiarities of real-world datasets to yield reliable individualized risk evaluations. (duke.edu)
• In Aim 3, to conduct flexible and reliable inference on the estimated cost trajectory, we developed a longitudinal varying coefficient single-index model and computational optimization algorithm that is scalable to baseline feature inference with noise. (tmc.edu)
• The brief lesson is this: too many factors mean too much redundant information which 'contaminates' statistical inference through extra noise. (lu.se)

Approaches2

• This dissertation studies novel statistical machine learning approaches for healthcare risk prediction applications in the presence of challenging scenarios, such as rare events, noisy observations, data imbalance, missingness and censoring. (duke.edu)
• Comparing the statistical model to a camera, i.e., a tool to produce an image of reality, we contrast the deductive and predictive (minimal) modeling approaches on a dataset from a corpus study. (lu.se)

Predictive2

• A statistical model validation process was used to determine predictive confidence bounds for mass and natural frequency predictions applied to 1) stamped crossmember and 2) truck frame finite element models. (sae.org)
• Such scenarios manifest frequently in practice, and they compromise the validity of standard predictive models which often expect clean and complete data. (duke.edu)

Approach3

• Such a non-empirical approach may lead to misspecification of the Q-matrix and substantial lack of model fit, resulting in erroneous interpretation of testing results. (columbia.edu)
• Multistage modeling of leukemia in benzene workers: a simple approach to fitting the 2-stage clonal expansion model. (cdc.gov)
• We advocate an alternative, "deductive modeling" approach that retains a "full" model of variables generated from our research questions and objectives. (lu.se)

Numerical2

• Groundwater-level data, in conjunction with attendant metadata and covariates (predictor variables) data, for the Mississippi River Valley alluvial aquifer (MRVA) are used to support statistical and process-based numerical modeling. (usgs.gov)
• We perform numerical simulations of the model for two-dimensional lattice and reveal that the mass distribution for small- and intermediate-size fragments obeys a power law, F(m)∝m[superscript −3/2], in agreement with experimental observations. (le.ac.uk)

Estimation2

• Estimation in and discrimination among the various models are considered, followed by a small sampling experiment into the procedures and an example of their application. (who.int)
• If these unobservables are correlated with the observed indicators (change in labor force), then estimation of even the simplest econometric models becomes challenging. (lu.se)

Regression models1

• Multivariable Cox Proportional Hazards ( PH ) regression models and survival random forests (SRF) were built to identify the most influential factors. (bvsalud.org)

Methods1

• Existing methods of generating small area estimates often require advanced statistical knowledge, programming and coding skills, and extensive computing power. (cdc.gov)

Thesis2

• The second part of the thesis focuses on statistical validation of the Q-matrix. (columbia.edu)
• The third part of this thesis is concerned with the identifiability of the diagnostic model parameters when the Q-matrix is correctly specified. (columbia.edu)

Factors4

• Risk and protective factors in Parkinson's disease: a simultaneous and prospective study with classical statistical and novel machine learning models. (bvsalud.org)
• No study has prospectively and simultaneously investigated potential risk / protective factors of PD using both classical statistical and novel machine learning analyses. (bvsalud.org)
• The latter may reveal more complex associations and new factors that are undetected by merely linear models . (bvsalud.org)
• A variety of social, political, and economic factors influenced the creation of the first Diagnostic and Statistical Manual of Mental Disorders (DSM) in 1952. (bvsalud.org)

Workshop2

• A joint workshop co-organised between UCL and the Institute of Statistical Mathetmatics in Tokyo will be running from 1-3 August 2013. (ucl.ac.uk)
• The AIDS epidemic and its demographic consequences : proceedings of the United Nations/World Health Organization Workshop on Modelling the Demographic Impact of the AIDS Epidemic in Pattern II Countries: Progress to Date and Policies for the Future, New York, 13-15 December 1989. (who.int)

Data23

• One important domain is descriptive statistics , which summarizes data using statistical measures of central tendency and dispersion. (pluralsight.com)
• The basic data structures are intended also to serve as interpretability standards for use by statistical software such as described by Asquith and Seanor (2019) and Asquith and others (2019). (usgs.gov)
• We propose a test statistic that measures the consistency of observed data with the proposed Q-matrix. (columbia.edu)
• We present sufficient and necessary conditions under which the model parameters are identifiable from the response data. (columbia.edu)
• This dissertation starts with a systematic review of classical risk prediction models in Chapter 1, and discusses the new opportunities and challenges presented by the big data era. (duke.edu)
• With the increasing availability of healthcare data and the current rapid development of machine learning models, clinical decision support systems have seen new opportunities to improve clinical practice. (duke.edu)
• However, in healthcare risk prediction applications, statistical analysis is not only challenged by data incompleteness and skewed distributions but also the complexity of the inputs. (duke.edu)
• We investigate a more generalized setting of a multi-classification problem with heavily imbalanced data in Chapter 3, from the perspective of causal machine learning to promote sample efficiency and model generalization. (duke.edu)
• The effectiveness and enhanced prediction accuracy are demonstrated through synthetic data and real-world benchmarks compared with state-of-art models. (duke.edu)
• To better handle sophisticated modern health data and alleviate the impact of real-world data challenges, we introduce a self-attention based model to capture helpful information for time-to-event prediction, called Energy-based Latent Self-Attentive Survival Analysis (ELSSA). (duke.edu)
• In the third model, we use a generalized additive model (GAM) to analyze the data as a time series indexed by day, with covariates such as NDVI, latitude, longitude etc. (uwo.ca)
• Model predictions for the change in free energy of adsorption to zwitterionic membrane are in good agreement with previously reported experimental data with liposomes. (edpsciences.org)
• A likelihood ratio or Wald X² test formally tests the hypothesis of whether the model fits the data better than no model. (analyse-it.com)
• It is common to test whether the model fits the data better than the null model with no parameters. (analyse-it.com)
• So, for Aim 2, we developed a flexible model to address further challenges such as heteroscedasticity without imposing a cost data distributional assumption. (tmc.edu)
• This example uses heart disease mortality data for North Carolina census tracts to map the RST output, including reliability of estimates, and demonstrates a subsequent statistical test. (cdc.gov)
• Epidemic models : their structure and relation to data / edited by Denis Mollison. (who.int)
• Models for infectious human diseases : their structure and relation to data / edited by Valerie Isham, Graham Medley. (who.int)
• Multiple imputation is a statistical technique in which missing data are replaced by several sets of plausible, alternative simulated values. (cdc.gov)
• This analysis shows that the 2-stage clonal expansion model can be readily fit to epidemiologic cohort data by using a simple SAS program. (cdc.gov)
• Using quarterly data from 2000 through 2010, we estimated dynamic panel data models for employment and sales in restaurants and bars. (cdc.gov)
• For the county models, we combined data from all counties for which data were available, whether smoke-free or not, and compared the average effect of smoke-free laws in counties that contain smoke-free communities with counties that have no smoke-free communities. (cdc.gov)
• We demonstrated the use of remote sensing data in time-series to develop a model of dengue incidence in the MDR of Viet Nam. (who.int)

Analyses1

• For the crossmember, modal performance measure sensitivity to variation parameters was determined using a designed experiment based on a set of 121 finite element model analyses and field tests of 7 hardware samples, A similar experiment based on a set of 8 finite element model analyses and field tests of 8 hardware samples of the truck frame was conducted. (sae.org)

Analysis4

• Statistical analysis is a crucial part of any business intelligence function. (pluralsight.com)
• The results and analysis strategies are general in the sense that they can be further extended to other diagnostic models. (columbia.edu)
• This model acts as a starting point for a deeper analysis of consumer and producer behaviour. (lu.se)
• One early impetus for the creation of the DSM lies in the advent of the Census in 1790, a year after George Washington's inauguration, by the marshals of the US judicial districts (United States Census Bureau, 2013) leading to gathering information about each member of society in a format amenable to statistical analysis. (bvsalud.org)

Linear Regression1

• The second model is a functional linear regression model (FLRM), where we use NDVI as the covariate. (uwo.ca)

20211

• 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)

Estimates3

• A random effect model was used to combine all estimates into a weighted average with 95%-confidence interval. (ersjournals.com)
• Accurate and precise estimates of local-level epidemiologic measures are critical to informing policy and program decisions, but they often require advanced statistical knowledge, programming/coding skills, and extensive computing power. (cdc.gov)
• 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)

Observations1

• A simple SAS software program (SAS Institute, Inc., Cary, North Carolina) was developed for fitting an exact formulation of the 2-stage clonal expansion model accommodating piecewise constant exposures and left and right censoring of observations. (cdc.gov)

Results2

• In addition to reviewing the RST's statistical techniques, we present results from a simulation study that illustrates the key benefit of smoothing. (cdc.gov)
• Results of search for 'su:{Models, Statistical. (who.int)

Abstract1

• ABSTRACT A cluster random sample of 1073 students in the Islamic Republic of Iran (mean age 14.37 years) completed a self-report questionnaire consisting of an exercise measure, selected constructs of Pender's health promotion model and an assessment of stages of behaviour change in exercise. (who.int)

Subsequent1

• The steps for creating the above statistical measures are explained in the subsequent sections. (pluralsight.com)

Diagnostic4

• Diagnostic classification models (DCM) are an important recent development in educational and psychological testing. (columbia.edu)
• In addition, we study the identifiability of diagnostic model parameters under a known Q-matrix. (columbia.edu)
• We considered all versions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) from the first one published in 1952 to the latest revision published in mid-2013. (bvsalud.org)
• Although the first Diagnostic and Statistical Manual of Mental Disorders (DSM) was not published until 1952, there is a great deal of history and a collection of driving forces that are responsible for its publication. (bvsalud.org)

Experiments4

• Analytic relations describing these experiments are valuable, and these relations can be obtained through the statistical thermodynamics of idealized model systems representing the experiments. (arxiv.org)
• Since the specific thermodynamic ensembles manifested by the experiments affect the outcome, primarily for small molecules, the stretching device must be included in the idealized model system. (arxiv.org)
• Experiments with model membrane systems have indicated, however, that membrane cholesterol reduces apo A-I binding to the membrane. (edpsciences.org)
• We develop an analytical theory which explains the detected power law and demonstrate that the overall fragment mass distribution in our model agrees qualitatively with that one observed in experiments. (le.ac.uk)

Equations1

• type of model, called a statistical-dynamical model, forecasts the large-scale circulation by solving equations that describe changes in atmospheric pressure, wind, and moisture. (britannica.com)

Methodology1

• Particularly, we rigorously adapted his methodology to factor models and CCE technique. (lu.se)

Epidemic1

• Statistical forecast models play a role in predicting future epidemic threats, managing of societal, economic, cultural, and public health matters. (who.int)

Commonly1

• In particular, we present definitive answers to the learnability of the Q-matrix for one of the most commonly used models, the DINA model, by specifying a set of sufficient conditions under which the Q-matrix is identifiable up to an explicitly defined equivalence class. (columbia.edu)

Calculations1

• The asymptotic theories are first demonstrated to be accurate using the freely jointed chain model, and then using molecular dynamics calculations of a single polyethylene chain. (arxiv.org)

Test2

• Statistical measures were used to compare and test distributions of computer model and field outputs with the intent of computing confidence bounds for the selected performance statistics. (sae.org)
• Theoretical properties of the test statistic are studied. (columbia.edu)

Chapter1

• Some models may be old-fashioned, whilst some others have been further extended or developed so as to better address special research questions presented in each chapter of the book. (novapublishers.com)

Probability3

• Developed, built, and tested a model grounded in probability theory that accurately predicts bias and polarization in highly decentralized communities, in consultation with Joe Benson, Ph.D., Professor of Mathematics at Macalester College. (dribbble.com)
• It will involve several experts from the fields of statistical science, signal processing engineering and probability. (ucl.ac.uk)
• Several models for limited dependent variables (variables having a non-negligible probability of exactly equaling zero) are examined. (who.int)

Procedure2

• The purpose of this study is to provide a statistical procedure to help decide whether to accept the Q-matrix provided by the experts. (columbia.edu)
• While a minimal adequate model is more parsimonious, its selection procedure is blind to the research aim and may conceal relevant information. (lu.se)

Effect2

• How to build a model to estimate the annual effect and seasonal dynamics is a challenging task. (uwo.ca)
• Here we present a statistical mechanical model to account for the effect of cholesterol. (edpsciences.org)

Variation1

• Likelihood ratio tests and maximum likelihood estimators are derived for simple models which describe this variation. (dtic.mil)

Year1

• In modeling single sites, we treat each year as a multivariate observation. (uwo.ca)

Transmission1

• Modelling onchocerciasis transmission and control / Anton Paul Plaisier. (who.int)

Application1

• De Clerck, J. and Doerr, D., "Application of Statistical Model Validation to a Truck Frame," SAE Technical Paper 2006-01-0281, 2006, https://doi.org/10.4271/2006-01-0281 . (sae.org)

Risk2

• Title : A Statistical Model for Assessing Genetic Susceptibility as a Risk Factor in Multifactorial Diseases: Lessons from Occupational Asthma Personal Author(s) : Demchuk, Eugene;Yucesoy, Berran;Johnson, Victor J.;Andrew, Michael;Weston, Ainsley;Germolec, Dori R.;De Rosa, Christopher T.;Luster, Michael I. (cdc.gov)
• Cox PH models revealed that age, sex , dysthyroidism and diabetes were associated with an increased risk of PD. (bvsalud.org)

Research1

• Technical aspects of each statistical model are presented in detail to allow readers to follow and apply their own research and practices. (novapublishers.com)

Shows2

• The FLRM also shows a great potential for modeling with limited information. (uwo.ca)
• A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. (who.int)

Full1

• Here, a dual set of asymptotically correct statistical thermodynamic theories are applied to develop accurate approximations for the full model system that includes both the molecule and the device. (arxiv.org)

Single1

• We use parametric bootstrap to validate our single location modeling on 55 flux sites. (uwo.ca)