126 research outputs found
Lessons learned from the Pefki solar village in Athens, nearly 20 years on
Solar Village 3 in Pefki, Athens, was part of an ambitious program, with active and passive solar systems providing space and water heating for 1750 inhabitants, designed in the early 80's, and inhabited from the late 80's. This paper focuses on passive solar systems applied to a number of the buildings. A survey highlighted the cases of trombe water benches and conservatories as the most frequently, poorly operated systems. Over time this led to a lack of belief by the occupants in the passive systems. Building simulation indicated a much higher cooling load than originally designed for, combined with recent warmer summers and poor maintenance and operation, have led to the present case that many homes have installed air conditioning. Plans for district heating will improve heating provision for residents and reduce CO2 emissions but a lack of a maintenance strategy for the passive systems will surely lead to their increased neglect
Robust and Adaptive Functional Logistic Regression
We introduce and study a family of robust estimators for the functional
logistic regression model whose robustness automatically adapts to the data
thereby leading to estimators with high efficiency in clean data and a high
degree of resistance towards atypical observations. The estimators are based on
the concept of density power divergence between densities and may be formed
with any combination of lower rank approximations and penalties, as the need
arises. For these estimators we prove uniform convergence and high rates of
convergence with respect to the commonly used prediction error under fairly
general assumptions. The highly competitive practical performance of our
proposal is illustrated on a simulation study and a real data example which
includes atypical observations
Asymptotics for M-type smoothing splines with non-smooth objective functions
M-type smoothing splines are a broad class of spline estimators that include
the popular least-squares smoothing spline but also spline estimators that are
less susceptible to outlying observations and model-misspecification. However,
available asymptotic theory only covers smoothing spline estimators based on
smooth objective functions and consequently leaves out frequently used
resistant estimators such as quantile and Huber-type smoothing splines. We
provide a general treatment in this paper and, assuming only the convexity of
the objective function, show that the least-squares (super-)convergence rates
can be extended to M-type estimators whose asymptotic properties have not been
hitherto described. We further show that auxiliary scale estimates may be
handled under significantly weaker assumptions than those found in the
literature and we establish optimal rates of convergence for the derivatives,
which have not been obtained outside the least-squares framework. A simulation
study and a real-data example illustrate the competitive performance of
non-smooth M-type splines in relation to the least-squares spline on regular
data and their superior performance on data that contain anomalies
Robust Functional Regression with Discretely Sampled Predictors
The functional linear model is an important extension of the classical
regression model allowing for scalar responses to be modeled as functions of
stochastic processes. Yet, despite the usefulness and popularity of the
functional linear model in recent years, most treatments, theoretical and
practical alike, suffer either from (i) lack of resistance towards the many
types of anomalies one may encounter with functional data or (ii) biases
resulting from the use of discretely sampled functional data instead of
completely observed data. To address these deficiencies, this paper introduces
and studies the first class of robust functional regression estimators for
partially observed functional data. The proposed broad class of estimators is
based on thin-plate splines with a novel computationally efficient quadratic
penalty, is easily implementable and enjoys good theoretical properties under
weak assumptions. We show that, in the incomplete data setting, both the sample
size and discretization error of the processes determine the asymptotic rate of
convergence of functional regression estimators and the latter cannot be
ignored. These theoretical properties remain valid even with multi-dimensional
random fields acting as predictors and random smoothing parameters. The
effectiveness of the proposed class of estimators in practice is demonstrated
by means of a simulation study and a real-data example
M-type penalized splines with auxiliary scale estimation
Penalized spline smoothing is a popular and flexible method of obtaining
estimates in nonparametric regression but the classical least-squares criterion
is highly susceptible to model deviations and atypical observations. Penalized
spline estimation with a resistant loss function is a natural remedy, yet to
this day the asymptotic properties of M-type penalized spline estimators have
not been studied. We show in this paper that M-type penalized spline estimators
achieve the same rates of convergence as their least-squares counterparts, even
with auxiliary scale estimation. We further find theoretical justification for
the use of a small number of knots relative to the sample size. We illustrate
the benefits of M-type penalized splines in a Monte-Carlo study and two
real-data examples, which contain atypical observations
Robust functional regression based on principal components
Functional data analysis is a fast evolving branch of modern statistics and
the functional linear model has become popular in recent years. However, most
estimation methods for this model rely on generalized least squares procedures
and therefore are sensitive to atypical observations. To remedy this, we
propose a two-step estimation procedure that combines robust functional
principal components and robust linear regression. Moreover, we propose a
transformation that reduces the curvature of the estimators and can be
advantageous in many settings. For these estimators we prove Fisher-consistency
at elliptical distributions and consistency under mild regularity conditions.
The influence function of the estimators is investigated as well. Simulation
experiments show that the proposed estimators have reasonable efficiency,
protect against outlying observations, produce smooth estimates and perform
well in comparison to existing approaches.Comment: 33 pages, including the appendix and reference
The law of activity delays
Delays in activities completion drive human projects to schedule and cost
overruns. It is believed activity delays are the consequence of multiple
idiosyncrasies without specific patterns or rules. Here we show that is not the
case. Using data for 180 construction project schedules, we demonstrate that
activity delays satisfy a universal model that we call the law of activity
delays. After we correct for delay risk factors, what remains follows a
log-normal distribution.Comment: 7 pages, 4 figures, 1 tabl
Robust and efficient estimation of nonparametric generalized linear models
Generalized linear models are flexible tools for the analysis of diverse
datasets, but the classical formulation requires that the parametric component
is correctly specified and the data contain no atypical observations. To
address these shortcomings we introduce and study a family of nonparametric
full rank and lower rank spline estimators that result from the minimization of
a penalized power divergence. The proposed class of estimators is easily
implementable, offers high protection against outlying observations and can be
tuned for arbitrarily high efficiency in the case of clean data. We show that
under weak assumptions these estimators converge at a fast rate and illustrate
their highly competitive performance on a simulation study and two real-data
examples
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