Classical Regression Models for Competing Risks

2016 ◽  
pp. 169-189
Keyword(s):  
Competing Risks ◽  
Regression Models ◽  
Classical Regression

scholarly journals Analysis of Prices in the Housing Market Using Mixed Models

2018 ◽  
Vol 26 (4) ◽  
pp. 102-111 ◽  
Author(s):  
Aneta Cichulska ◽  
Radosław Cellmer
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Data ◽  
Mixed Models ◽  
Fixed Effects ◽  
Regression Models ◽  
Mixed Model ◽  
Property Market ◽  
The Hierarchical Structure ◽  
Mixed Modelling ◽  
Classical Regression

Abstract Hedonic models, commonly applied for analyzing prices in the property market, do not always fulfil their role, mainly due to the application of simplified assumptions concerning the distribution of variables, the nature of relations or spatial heterogeneity. Classical regression models assumed that the variation of the explained variable (price) is explained by the effect of market features (fixed effects) and the residual component. The hierarchical structure of market data, both as regards market segments and the spatial division, suggests that statistical models of prices should also include random effects for selected subgroups of properties and interactions between variables. The mixed model provides an alternative for constructing various regression models for individual groups or for using binary variables within one model. With its appropriate structure, it makes it possible to take into account both the spatial heterogeneity and to examine the effects of individual features on prices within various property groups. It can also identify synergy effects. The article presents the issue of mixed modelling in the property market and an example of its application in a market of dwellings in Olsztyn. The research used transaction data from the price and value register, supplemented with spatial data. The obtained model was compared with classical regression models and geographically weighted regression. The study also covered the usefulness of mixed models in the mass evaluation of properties, and the possibility of using them in spatial analyses and for the development of property value maps.

scholarly journals Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2030
Author(s):  
Ali Mohammed Baba ◽  
Habshah Midi ◽  
Mohd Bakri Adam ◽  
Nur Haizum Abd Rahman
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Spatial Regression ◽  
Spatial Models ◽  
Influential Observations ◽  
Leverage Points ◽  
Cook’S Distance ◽  
Spatial Regression Models ◽  
Cook's Distance ◽  
Classical Regression

Influential observations (IOs), which are outliers in the x direction, y direction or both, remain a problem in the classical regression model fitting. Spatial regression models have a peculiar kind of outliers because they are local in nature. Spatial regression models are also not free from the effect of influential observations. Researchers have adapted some classical regression techniques to spatial models and obtained satisfactory results. However, masking or/and swamping remains a stumbling block for such methods. In this article, we obtain a measure of spatial Studentized prediction residuals that incorporate spatial information on the dependent variable and the residuals. We propose a robust spatial diagnostic plot to classify observations into regular observations, vertical outliers, good and bad leverage points using a classification based on spatial Studentized prediction residuals and spatial diagnostic potentials, which we refer to as and . Observations that fall into the vertical outliers and bad leverage points categories are referred to as IOs. Representations of some classical regression measures of diagnostic in general spatial models are presented. The commonly used diagnostic measure in spatial diagnostics, the Cook’s distance, is compared to some robust methods, (using robust and non-robust measures), and our proposed and plots. Results of our simulation study and applications to real data showed that the Cook’s distance, non-robust and robust were not very successful in detecting IOs. The suffered from the masking effect, and the robust suffered from swamping in general spatial models. Interestingly, the results showed that the proposed plot, followed by the plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs.

scholarly journals Comparison Between Urothelial and Non-Urothelial Urethral Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Mike Wenzel ◽  
Marina Deuker ◽  
Luigi Nocera ◽  
Claudia Collà Ruvolo ◽  
Zhe Tian ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Competing Risks ◽  
Regression Models ◽  
Cox Regression ◽  
Tumor Characteristics ◽  
Kaplan Meier ◽  
Specific Mortality ◽  
Variant Histology ◽  
Cancer Specific Mortality ◽  
Overall Mortality

BackgroundTo test the effect of variant histology relative to urothelial histology on stage at presentation, cancer specific mortality (CSM), and overall mortality (OM) after chemotherapy use, in urethral cancer.Materials and MethodsWithin the Surveillance, Epidemiology and End Results (2004–2016) database, we identified 1,907 primary variant histology urethral cancer patients. Kaplan-Meier plots, Cox regression analyses, cumulative incidence-plots, multivariable competing-risks regression models and propensity score matching for patient and tumor characteristics were used.ResultsOf 1,907 eligible urethral cancer patients, urothelial histology affected 1,009 (52.9%) vs. squamous cell carcinoma (SCC) 455 (23.6%) vs. adenocarcinoma 278 (14.6%) vs. other histology 165 (8.7%) patients. Urothelial histological patients exhibited lower stages at presentation than SCC, adenocarcinoma or other histology patients. In urothelial histology patients, five-year CSM was 23.5% vs. 34.4% in SCC [Hazard Ratio (HR) 1.57] vs. 40.7% in adenocarcinoma (HR 1.69) vs. 43.4% in other histology (HR 1.99, p < 0.001). After matching in multivariate competing-risks regression models, variant histology exhibited 1.35-fold higher CSM than urothelial. Finally, in metastatic urethral cancer, lower OM was recorded after chemotherapy in general, including metastatic adenocarcinoma and other variant histology subtypes, except metastatic SCC.ConclusionAdenocarcinoma, SCC and other histology subtypes affect fewer patients than urothelial histology. Presence of variant histology results in higher CSM. Finally, chemotherapy for metastatic urethral cancer improves survival in adenocarcinoma and other variant histology subtypes, but not in SCC.

scholarly journals On the variability of technological indicators in the extraction of precious metals

2021 ◽  
Vol 266 ◽  
pp. 02003
Author(s):  
E. K. Ushakov ◽  
T. N. Alexandrova
Keyword(s):  
Regression Models ◽  
Network Modeling ◽  
Precious Metals ◽  
Neural Network Modeling ◽  
Flotation Process ◽  
Significant Variability ◽  
Modeling Methodology ◽  
The Neural Network ◽  
Silver Extraction ◽  
Classical Regression

in the conditions of significant variability of processed polymetallic ores of the Akbastau Deposit, it is essential to minimize the variability of technological indicators of enrichment. Due to the multifactorial nature and non-linearity of the flotation process, the use of classical regression models does not provide the necessary level of reliability, therefore, there is a significant variability in the extraction of precious metals. To solve this problem, the paper substantiates the use of the neural network modeling methodology, which allows to estimate the variability of gold and silver extraction depending on the variation of the content of metals in the ore.

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