scholarly journals Obesity and Health-Care Costs in Switzerland: Dealing with Endogeneity in Non-Linear Regression Models

2016 ◽  
Vol 152 (3) ◽  
pp. 243-286
Author(s):  
Stefan Meyer
Keyword(s):  
Health Care ◽  
Linear Regression ◽  
Health Care Costs ◽  
Regression Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Care Costs

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

scholarly journals Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 130
Author(s):  
Omar Rodríguez-Abreo ◽  
Juvenal Rodríguez-Reséndiz ◽  
L. A. Montoya-Santiyanes ◽  
José Manuel Álvarez-Alvarado
Keyword(s):  
Linear Regression ◽  
Vibration Amplitude ◽  
Regression Models ◽  
Engineering Problem ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Gray Wolf ◽  
Linear Regression Models ◽  
Machinery Condition Monitoring ◽  
Non Linear

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

Non-Linear Regression Models to Predict the Lamb and Sheep Weight Growth

2013 ◽  
Vol 12 (9) ◽  
pp. 865-869 ◽  
Author(s):  
Muhammad Aman Ullah ◽  
Muhammad Amin ◽  
Muhammad Ansar Abbas
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Weight Growth ◽  
Non Linear

Associations between perceptions of care experiences and receipt of mental health care among older adults with cancer.

2020 ◽  
Vol 38 (29_suppl) ◽  
pp. 175-175
Author(s):  
Lisa M Lines ◽  
Daniel H Barch ◽  
Diana Zabala ◽  
Michael T. Halpern ◽  
Paul Jacobsen ◽  
...  
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Health Care ◽  
Linear Regression ◽  
Mental Health Care ◽  
Mood Disorders ◽  
Regression Models ◽  
Case Mix ◽  
Linear Regression Models ◽  
Fair Poor

175 Background: Older adults with cancer and worse self-rated mental health report worse care experiences. We hypothesized that, controlling for health and demographic characteristics, older adults with cancer who received care for anxiety or mood disorders would report better care experiences. Methods: We used SEER-CAHPS data to identify Medicare beneficiaries, aged 66 and over, diagnosed from August 2006 through December 2013 with one of the 10 most prevalent solid tumor malignancies. To identify utilization for anxiety or mood disorders (screening, diagnosis, or treatment), we analyzed inpatient, outpatient, home health, physician, and prescription drug claims from 12 months before through up to 5 years after cancer diagnosis. Outcomes of interest were global care experience ratings (Overall Care, Personal Doctor, and Specialist; rated on a 0-10 scale) and composite measures (Getting Needed Care, Getting Care Quickly, and Doctor Communication; scored from 0-100). We estimated linear regression models and also used a Bayesian Model Averaging approach, adjusting for standard case-mix adjustors (including sociodemographics and self-reported general health and mental health status [MHS]) and other characteristics, including cancer site and stage at diagnosis. We also included interaction terms between mental health care utilization and MHS. Results: Approximately 22% of the overall sample (n = 4,998) had both cancer and a claim for an anxiety or mood disorder, and of those individuals, 18% reported fair/poor MHS. Only 7% of those in the cancer-only cohort reported fair/poor MHS. Before adjusting for mental health utilization, worse MHS was significantly associated with worse experience of care. After accounting for anxiety/mood disorder-related utilization, linear regression models showed no significant associations between fair/poor MHS and worse care experiences, while Bayesian models found that reliable associations remained between worse MHS and lower global ratings of Overall Care and Specialist. Conclusions: Utilization for anxiety/mood disorders mediates the association between fair/poor MHS and worse care experiences. Although MHS is a case-mix adjustor for CAHPS public reporting, it is important to recognize that care for anxiety or mood disorders may improve care experiences among seniors with cancer.

Meta-heuristic to estimate parameters in Non-Linear Regression Models

2011 ◽  
Vol 3 (5) ◽  
pp. 473
Author(s):  
K. Antony Arokia Durai Raj ◽  
B. Kanagasabapathi ◽  
Gopichand Agnihothram
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Non Linear
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