Conditional quantile regression models of melanoma tumor growth curves for assessing treatment effect in small sample studies

2014 ◽  
Vol 33 (29) ◽  
pp. 5209-5220 ◽  
Author(s):  
Ella Revzin ◽  
Dibyen Majumdar ◽  
Gilbert W. Bassett
Keyword(s):  
Tumor Growth ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Regression Models ◽  
Growth Curves ◽  
Small Sample ◽  
Melanoma Tumor ◽  
Conditional Quantile

scholarly journals Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models with Fixed Effects

2020 ◽  
Author(s):  
Fernando Rios-Avila ◽  
Michelle Lee Maroto
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Regression Models ◽  
Wage Distribution ◽  
Motherhood Penalty ◽  
Unconditional Quantile Regression ◽  
Quantile Treatment Effect

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's (2010) work on the motherhood penalty using NLSY79 data.

scholarly journals Quantile Regression Models and the Crucial Difference Between Individual-Level Effects and Population-Level Effects

2020 ◽  
Author(s):  
Nicolai T. Borgen ◽  
Andreas Haupt ◽  
Øyvind N. Wiborg
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Treatment Effects ◽  
Population Level ◽  
Conditional Quantile ◽  
Individual Level ◽  
Quantile Treatment Effects ◽  
Crucial Difference ◽  
Practical Guidelines ◽  
Conceptual Distinction

The unconditional quantile regression (UQR) model – which has gained increasing popularity in the 2010s and is regularly applied in top-rated academic journals within sociology and other disciplines – is poorly understood and frequently misinterpreted. The main reason for its increased popularity is that the UQR model seemingly tackles an issue with the traditional conditional quantile regression (CQR) model: the interpretation of coefficients as quantile treatment effects changes whenever control variables are included. However, the UQR model was not developed to solve this issue but to study influences on quantile values of the overall outcome distribution. This paper clarifies the crucial conceptual distinction between influences on overall distributions, which we term population-level influences, and individual-level quantile treatment effects. Further, we use data simulations to illustrate that various classes of quantile regression models may, in some instances, give entirely different conclusions (to different questions). The conceptual and empirical distinctions between various quantile regression models underline the need to match the correct quantile regression model to the specific research questions. We conclude the paper with some practical guidelines for researchers.

Testing exogeneity in nonparametric instrumental variables models identified by conditional quantile restrictions

2020 ◽  
Author(s):  
Jia-Young Michael Fu ◽  
Joel L Horowitz ◽  
Matthias Parey
Keyword(s):  
Quantile Regression ◽  
Instrumental Variables ◽  
Regression Models ◽  
Quantile Function ◽  
Structural Function ◽  
Conditional Quantile ◽  
Explanatory Variables ◽  
Monte Carlo Experiments ◽  
Nonparametric Estimates ◽  
Conditional Quantile Function

Summary This paper presents a test for exogeneity of explanatory variables in a nonparametric instrumental variables (IV) model whose structural function is identified through a conditional quantile restriction. Quantile regression models are increasingly important in applied econometrics. As with mean-regression models, an erroneous assumption that the explanatory variables in a quantile regression model are exogenous can lead to highly misleading results. In addition, a test of exogeneity based on an incorrectly specified parametric model can produce misleading results. This paper presents a test of exogeneity that does not assume that the structural function belongs to a known finite-dimensional parametric family and does not require estimation of this function. The latter property is important because nonparametric estimates of the structural function are unavoidably imprecise. The test presented here is consistent whenever the structural function differs from the conditional quantile function on a set of nonzero probability. The test has nontrivial power uniformly over a large class of structural functions that differ from the conditional quantile function by $O({n^{ - 1/2}})$. The results of Monte Carlo experiments and an empirical application illustrate the performance of the test.

scholarly journals Efficiency of Bayesian Approaches in Quantile Regression with Small Sample Size

2018 ◽  
pp. 1-13
Author(s):  
Neveen Sayed-Ahmed
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Full Range ◽  
Small Sample ◽  
Adaptive Lasso ◽  
Sample Sizes ◽  
Bayesian Approaches ◽  
Conditional Quantile ◽  
Quantile Functions ◽  
Small Sample Sizes

Quantile regression is a statistical technique intended to estimate, and conduct inference about the conditional quantile functions. Just as the classical linear regression methods estimate model for the conditional mean function, quantile regression offers a mechanism for estimating models for the conditional median function, and the full range of other conditional quantile functions. In the Bayesian approach to variable selection prior distributions representing the subjective beliefs about the parameters are assigned to the regression coefficients. The estimation of parameters and the selection of the best subset of variables is accomplished by using adaptive lasso quantile regression. In this paper we describe, compare, and apply the two suggested Bayesian approaches. The two suggested Bayesian suggested approaches are used to select the best subset of variables and estimate the parameters of the quantile regression equation when small sample sizes are used.  Simulations show that the proposed approaches are very competitive in terms of variable selection, estimation accuracy and efficient when small sample sizes are used.   

scholarly journals What Quantile Regression Does and Doesn't Do

2018 ◽  
Author(s):  
Sebastian Ernst Wenz
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Models ◽  
Simulated Data ◽  
Quantile Function ◽  
Linear Regression Models ◽  
Conditional Quantile ◽  
Correct Understanding ◽  
Mean Function ◽  
Conditional Quantile Function

Petscher and Logan (2014)’s description of quantile regression might mislead readers to believe it would estimate the relation between an outcome, y, and one or more predictors, x, at different quantiles of the unconditional distribution of y. However, quantile regression models the conditional quantile function of y given x just as linear regression models the conditional mean function. This article’s contribution is twofold: First, it discusses potential consequences of methodological misconceptions and formulations of Petscher and Logan (2014)’s presentation by contrasting features of quantile regression and linear regression. Secondly, it reinforces the importance of correct understanding of quantile regression in empirical research by illustrating similarities and differences of various quantile regression estimators and linear regression using simulated data.

Synergistic inhibitory effects of Celecoxib and Plumbagin on melanoma tumor growth

2017 ◽  
Vol 385 ◽  
pp. 243-250 ◽  
Author(s):  
Raghavendra Gowda ◽  
Arati Sharma ◽  
Gavin P. Robertson
Keyword(s):  
Tumor Growth ◽  
Inhibitory Effects ◽  
Melanoma Tumor

scholarly journals Age and growth of franciscana dolphins, Pontoporia blainvillei (Cetacea: Pontoporiidae) incidentally caught off southern Brazil and northern Argentina

2010 ◽  
Vol 90 (8) ◽  
pp. 1493-1500 ◽  
Author(s):  
Silvina Botta ◽  
Eduardo R. Secchi ◽  
Mônica M.C. Muelbert ◽  
Daniel Danilewicz ◽  
Maria Fernanda Negri ◽  
...  
Keyword(s):  
Growth Models ◽  
Growth Curves ◽  
Small Sample ◽  
Age And Growth ◽  
Buenos Aires Province ◽  
Von Bertalanffy ◽  
Southern Brazil ◽  
Data Set ◽  
Males And Females ◽  
Pontoporia Blainvillei

Age and length data of 291 franciscana dolphins (Pontoporia blainvillei) incidentally captured on the coast of Rio Grande do Sul State (RS), southern Brazil, were used to fit growth curves using Gompertz and Von Bertalanffy growth models. A small sample of franciscanas (N = 35) from Buenos Aires Province (BA), Argentina, were used to see if there are apparent growth differences between the populations. Male and female franciscana samples from both areas were primarily (78–85%) <4 years of age. The Von Bertalanffy growth model with a data set that excluded animals <1 year of age provided the best fit to data. Based on this model, dolphins from the RS population reached asymptotic length at 136.0 cm and 158.4 cm, for males and females, respectively. No remarkable differences were observed in the growth trajectories of males and females between the RS and BA populations.

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