714 research outputs found
lassopack: Model selection and prediction with regularized regression in Stata
This article introduces lassopack, a suite of programs for regularized
regression in Stata. lassopack implements lasso, square-root lasso, elastic
net, ridge regression, adaptive lasso and post-estimation OLS. The methods are
suitable for the high-dimensional setting where the number of predictors
may be large and possibly greater than the number of observations, . We
offer three different approaches for selecting the penalization (`tuning')
parameters: information criteria (implemented in lasso2), -fold
cross-validation and -step ahead rolling cross-validation for cross-section,
panel and time-series data (cvlasso), and theory-driven (`rigorous')
penalization for the lasso and square-root lasso for cross-section and panel
data (rlasso). We discuss the theoretical framework and practical
considerations for each approach. We also present Monte Carlo results to
compare the performance of the penalization approaches.Comment: 52 pages, 6 figures, 6 tables; submitted to Stata Journal; for more
information see https://statalasso.github.io
Optimal Scaling transformations to model non-linear relations in GLMs with ordered and unordered predictors
In Generalized Linear Models (GLMs) it is assumed that there is a linear
effect of the predictor variables on the outcome. However, this assumption is
often too strict, because in many applications predictors have a nonlinear
relation with the outcome. Optimal Scaling (OS) transformations combined with
GLMs can deal with this type of relations. Transformations of the predictors
have been integrated in GLMs before, e.g. in Generalized Additive Models.
However, the OS methodology has several benefits. For example, the levels of
categorical predictors are quantified directly, such that they can be included
in the model without defining dummy variables. This approach enhances the
interpretation and visualization of the effect of different levels on the
outcome. Furthermore, monotonicity restrictions can be applied to the OS
transformations such that the original ordering of the category values is
preserved. This improves the interpretation of the effect and may prevent
overfitting. The scaling level can be chosen for each individual predictor such
that models can include mixed scaling levels. In this way, a suitable
transformation can be found for each predictor in the model. The implementation
of OS in logistic regression is demonstrated using three datasets that contain
a binary outcome variable and a set of categorical and/or continuous predictor
variables.Comment: 35 pages, 4 figure
Fisheries
This is the final version. Available from MCCIP via the DOI in this record
Sub-Typing of Rheumatic Diseases Based on a Systems Diagnosis Questionnaire
The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups.49 patients with a rheumatic disease were characterized using a systems diagnosis questionnaire containing 106 questions based on Chinese and Western medicine symptoms. Categorical principal component analysis (CATPCA) was used to discover differences in symptom patterns between the patients. Two Chinese medicine experts where subsequently asked to rank the Cold and Heat status of all the patients based on the questionnaires. These rankings were used to study the Cold and Heat symptoms used by these practitioners.The CATPCA analysis results in three dimensions. The first dimension is a general factor (40.2% explained variance). In the second dimension (12.5% explained variance) 'anxious', 'worrying', 'uneasy feeling' and 'distressed' were interpreted as the Internal disease stage, and 'aggravate in wind', 'fear of wind' and 'aversion to cold' as the External disease stage. In the third dimension (10.4% explained variance) 'panting s', 'superficial breathing', 'shortness of breath s', 'shortness of breath f' and 'aversion to cold' were interpreted as Cold and 'restless', 'nervous', 'warm feeling', 'dry mouth s' and 'thirst' as Heat related. 'Aversion to cold', 'fear of wind' and 'pain aggravates with cold' are most related to the experts Cold rankings and 'aversion to heat', 'fullness of chest' and 'dry mouth' to the Heat rankings.This study shows that the presented systems diagnosis questionnaire is able to identify groups of symptoms that are relevant for sub-typing patients with a rheumatic disease
Comparison of computational codes for direct numerical simulations of turbulent Rayleigh-B\'enard convection
Computational codes for direct numerical simulations of Rayleigh-B\'enard
(RB) convection are compared in terms of computational cost and quality of the
solution. As a benchmark case, RB convection at and in a
periodic domain, in cubic and cylindrical containers is considered. A dedicated
second-order finite-difference code (AFID/RBflow) and a specialized
fourth-order finite-volume code (Goldfish) are compared with a general purpose
finite-volume approach (OpenFOAM) and a general purpose spectral-element code
(Nek5000). Reassuringly, all codes provide predictions of the average heat
transfer that converge to the same values. The computational costs, however,
are found to differ considerably. The specialized codes AFID/RBflow and
Goldfish are found to excel in efficiency, outperforming the general purpose
flow solvers Nek5000 and OpenFOAM by an order of magnitude with an error on the
Nusselt number below . However, we find that alone is not
sufficient to assess the quality of the numerical results: in fact,
instantaneous snapshots of the temperature field from a near wall region
obtained for deliberately under-resolved simulations using Nek5000 clearly
indicate inadequate flow resolution even when is converged. Overall,
dedicated special purpose codes for RB convection are found to be more
efficient than general purpose codes.Comment: 12 pages, 5 figure
Mathematics in different settings: plenary panel.
When we think about the title âMathematics in different settingsâ, a number of questions arise. For example:
⢠How many mathematics are there â one or many? Is there a mathematics that is âprior toâ, or independent of, any setting?
⢠What (who) is it that makes settings âdifferentâ? And how does this relate to social differences among people?
⢠What is an appropriate typology of different settings â for research or for curriculum design purposes? Relatedly, we might ask: who decides what is âimportantâ?
⢠What is the nature of relations among policy arrangements, research and educational institutional settings?
⢠How are different settings represented in mathematics teaching and assessment?
⢠What is the relationship of mathematics education researchers to any setting
- âŚ