scholarly journals Aplicación de modelos de elección discreta regularizados para el análisis de los determinantes del consumo cultural en Colombia: el caso de los bienes del patrimonio cultural

Nova Economia ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 37-68
Author(s):  
Andrey David Ramos Ramírez ◽  
Nora Elena Espinal Monsalve
Keyword(s):  
Ratio Model ◽  
Continuation Ratio

Resumen En este artículo se analizan los determinantes de la asistencia y frecuencia de asistencia a museos y sitios de interés histórico en Colombia, utilizando los datos de la Encuesta de Consumo Cultural aplicada por el Departamento Administrativo Nacional de Estadística en 2014. Los modelos de regresión logística y el Continuation Ratio Model regularizados que se estiman permiten identificar automáticamente las variables relevantes para explicar las decisiones de consumo cultural, al tiempo que solucionan los problemas estadísticos asociados a la existencia de un número alto de variables explicativas, como es el caso de la inestabilidad de los estimadores, correlaciones espurias, multicolinealidad incidental y endogeneidad incidental, entre otros. Los resultados indican que la acumulación de capital cultural es el principal determinante del consumo cultural, validando así el cumplimiento del enfoque de la adicción racional en el contexto colombiano.

Gencrm: A New Command for Generalized Continuation-Ratio Models

2018 ◽  
Vol 18 (4) ◽  
pp. 924-936 ◽  
Author(s):  
Shawn Bauldry ◽  
Jun Xu ◽  
Andrew S. Fullerton
Keyword(s):  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Common Factors ◽  
Modeling Processes ◽  
Ratio Model ◽  
Complete Coverage ◽  
Link Functions ◽  
Continuation Ratio ◽  
Job Promotion ◽  
Parameter Constraints

A continuation-ratio model represents a variant of an ordered regression model that is suited to modeling processes that unfold in stages, particularly those in which a return to a previous stage is not possible (for example, educational attainment, job promotion, or disease progression). The parameters for covariates in continuation-ratio models may be constrained to be equal, vary by a set of common factors (that is, proportionality constraints), or freely vary across stages. Currently, there are three community-contributed commands that fit continuation-ratio models. Each of these commands fits some subset of continuation-ratio models involving parameter constraints, but none of them offer complete coverage of the range of possibilities. The new gencrm command expands the options for continuation-ratio models to include the possibility for some of or all the covariates to be constrained to be equal, to freely vary, or to vary by a set of common factors across stages. gencrm relies on Stata's maximum likelihood routines for estimation and avoids reshaping the data. gencrm includes options for three link functions (logit, probit, and cloglog) and supports Stata's multiple-imputation suites of commands.

Radiomics features of ground glass nodules tailored different pathological grades of lung adenocarcinoma.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23127-e23127
Author(s):  
C Zhang ◽  
Haoran Zhai ◽  
Lan He ◽  
Zai-Yi Liu ◽  
Yi-Long Wu ◽  
...  
Keyword(s):  
Smoking Status ◽  
Ground Glass ◽  
Low Grade ◽  
High Grade ◽  
Ratio Model ◽  
Intermediate Grade ◽  
Potential Predictor ◽  
Continuation Ratio ◽  
Radiomics Signature ◽  
Ground Glass Nodules

e23127 Background: Different pathological subtypes as well as different grades of adenocarcinoma based on the IASLC/ATS/ERS classification had been proven to be stage-independent predictor of survival. Radiomics features, as a novel analytic method, has been increasingly applied in variety cancer research and may be a potential predictor for preoperatively differentiating pathological grades of adenocarcinoma. Methods: Patients (pts) with radiological proved as solitary ground glass nodule were eligible in this study. Radiomics features derived from computed tomography (CT) images were extracted by Chinese Academy of Science. All pts will be categorized into three groups with lepidic predominance as low-grade, acinar and papillary predominance as intermediate-grade, micropapillary and solid predominance as high-grade. We used L1 penalized constrained continuation ratio model to select relevant radiomics features, and corresponding radiomics signature was constructed. Association between the radiomics signature and pathological grades of adenocarcinoma was explored using the Kruskal-Wallis test and C-index was performed to test the efficacy of differentiating. Results: 82 pts were included in this study. Low-grade, intermediate-grade and high-grade contained 15 (18.3%), 53 (64.6%), 14 (17.1%) pts respectively. 475 radiomics features were extracted from thin section CT image and 10 of them selected through L1 penalized constrained continuation ratio model composed radiomics signature which significantly associated with pathological grades (P < 0.0001). C-index for radiomics signature were 0.813 (95%CI 0.793-0.833). Since clinical characters including gender, age, smoking status, NSE, CEA and CYFRA21-1 were not associated with different grades of adenocarcinoma, we could not establish nomogram based on the radiomics signature and correlated clinical characters. Conclusions: Radiomics features only can be a potential predictor for preoperatively differentiating pathological grades of adenocarcinoma, which may be a more applicable clinical predictor for patients’ survival. Yet large sample sizes are warranted to confirm the results.

scholarly journals Modelling Tabular Data with an Ordered Outcome

1996 ◽  
Vol 1 (3) ◽  
pp. 1-10
Author(s):  
Vernon Gayle
Keyword(s):  
Model Building ◽  
Linear Models ◽  
Statistical Modelling ◽  
Categorical Variables ◽  
Sociological Research ◽  
Ratio Model ◽  
Ordered Categories ◽  
Continuation Ratio ◽  
Recent Developments ◽  
Log Linear

A large amount of data that is considered within sociological studies consists of categorical variables that lend themselves to tabular analysis. In the sociological analysis of data regarding social class and educational attainment, for example, the variables of interest can often plausibly be considered as having a substantively interesting order. Standard log-linear models do not take ordinality into account, thereby potentially they may disregard useful information. Analyzing tables where the response variable has ordered categories through model building has been problematic in software packages such as GLIM (Aitken et al., 1989). Recent developments in statistical modelling have offered new possibilities and this paper explores one option, namely the continuation ratio model which was initially reported by Fienberg and Mason (1979). The fitting of this model to data in tabular form is possible in GLIM although not especially trivial and by and large this approach has not been employed in sociological research. In this paper I outline the continuation ratio model and comment upon how it can be fitted to data by sociologists using the GLIM software. In addition I present a short description of the relative merits of such an approach. Presenting this paper in an electronic format facilitates the possibility of replicating the analysis. The data is appended to the paper in the appropriate format along with a copy of the GLIM transcript. A dumped GLIM4 file is also attached.

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