1,721,088 research outputs found
Parametric estimation of non-crossing quantile functions
Full text linkQuantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to extreme events. The presence of quantile crossing, however, prevented using the fitted model for prediction and extrapolation. In search of a possible solution, we first considered a different version of QR, in which the QR coefficients were described by parametric functions. This alleviated the crossing problem, but did not eliminate it completely. Finally, we exploited the imposed parametric structure to formulate a constrained optimization algorithm that enforced monotonicity. The proposed example showed how the relatively unexplored field of parametric quantile functions could offer new solutions to the long-standing problem of quantile crossing. Our approach is particularly convenient in situations, like the analysis of time series, in which the fitted model may be used to predict extreme quantiles or to perform extrapolation. The described estimator has been implemented in the R package qrcm
Robust estimation and regression with parametric quantile functions
No full textA new, broad family of quantile-based estimators is described, and theoretical and empirical evidence is provided for their robustness to outliers in the response. The proposed method can be used to estimate all types of parameters, including location, scale, rate and shape parameters, extremes, regression coefficients and hazard ratios, and can be extended to censored and truncated data. The described estimator can be utilized to construct robust versions of common parametric and semiparametric methods, such as linear (Normal) regression, generalized linear models, and proportional hazards models. A variety of significant results and applications is presented to show the flexibility of the proposed approach. The R package Qest implements the estimator and provides the necessary functions for model building, prediction, and inference
Non-crossing quantile regression via monotone B-spline varying coefficients
No full textQuantile regression can be used to obtain a nonparametric estimate
of a conditional quantile function. The presence of quantile crossing, however,
leads to an invalid distribution of the response and makes it dicult to use the
tted model for prediction. In this work, we show that crossing can be alleviated
or completely eliminated by explicit modeling of the regression coecients as a
function of the percentile values in (0,1). We illustrate the approach via a wellknown
dataset by emphasizing dierences with respect to the competitors
Joint modelling of non-crossing additive quantile regression via constrained B-spline varying coefficients
No full textWe present a unified framework able to fit the entire quantile process, namely to estimate simultaneously multiple non-crossing quantile curves. The framework relies on assuming each regression parameter varies smoothly across the percentile direction according to B-splines whose coefficients obey proper restrictions. Multiple linear and penalized smooth terms are allowed and the corresponding tuning parameters are estimated efficiently as part of the model fitting. Monotonicity and concavity constraints on the smoothed relationships are also easily accounted for in the framework. Simulation results provide evidence our proposal exhibits good statistical performance with respect to competitors while guaranteeing the non-crossing property and modest computational load. Analyses on a real dataset related to vocabulary size growth are presented to illustrate the model capability in practice
A new tuning parameter selector in lasso regression
No full textPenalized regression models are popularly used in high-dimensional data
analysis to carry out variable selction and model fitting simultaneously. Whereas
success has been widely reported in literature, their performance largely depend on
the tuning parameter that balances the trade-off between model fitting and sparsity.
In this work we introduce a new tuning parameter selction criterion based on the
maximization of the signal-to-noise ratio. To prove its effectiveness we applied it to
a real data on prostate cancer disease
A penalized approach to covariate selection through quantile regression coefficient models
No full textThe coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selection is quantile-specific, our approach permits using information on all quantiles simultaneously. We describe the estimator, provide simulation results and analyse the data that motivated the present article. The proposed approach is implemented in the qrcmNP package in R
Convective and stratiform precipitation: A PCA-based clustering algorithm for their identification
Full text linkThe increasing occurrence of flood events in some areas of the Southern Mediterranean area (e.g.,
Sicily), over the last few years, has contributed to raising the importance of characterizing such
events and identifying their causes
The neglected status of the vermetid reefs in the Mediterranean Sea: A systematic map
Full text linkStudied since late 1800, vermetid reefs are marine bioconstructions of well-acknowledged importance in the Mediterranean Sea. Despite their persistence being jeopardized in the whole basin, recent studies have referred to this bioconstruction as a neglected habitat. In this study, we assessed the neglected status of the Mediterranean vermetid reefs in the scientific literature producing a systematic map through a multi-method bibliometric protocol. Scopus and Web of Science databases were jointly used for data collection. Vermetid reefs publication rate (i.e., number of publications per year) was investigated compared to the other Mediterranean bio-constructions using ANOVA analysis and Zero-Inflated Poisson regression. Later, VOSviewer software was used to perform a bibliometric network analysis and for mapping visualization. The analysis aimed at investigating gaps, patterns, and trends of the vermetid reefs together with the other main Mediterranean bioconstructions (i.e., Astroides calycularis, Cladocora caespitosa and coralligenous formations, and sabellariid and Lithophyllum reefs). The ANOVA analysis of the number of publications from 1966 to 2020 found statistically significant differences between coralligenous and vermetid reefs publication rates in the 2006-2010, 2011-2015, and 2016-2020 timeframes and pointed out a clear before/after-2010 pattern in coralligenous publication rate, which was also confirmed by the Zero-Inflated Poisson regression model. The bibliometric network analysis of the bio-constructions literature revealed the same temporal pattern, with the vermetid reefs poorly investigated and weakly connected to newer research lines and conservation topics. Instead, coralligenous showed strong con-nections with biodiversity conservation and was indicated as a recent research hotspot. Overall, the results of this study confirm previous references of the vermetid reefs as a neglected habitat and, among others, show an increasing research interest in the coralligenous topic
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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