76,566 research outputs found
Rough Set Theory for Real Estate Appraisal: An Application to Directional District of Naples
This paper proposes an application of Rough Set Theory (RST) to the real estate field, in order to highlight its operational potentialities for mass appraisal purposes. RST allows one to solve the appraisal of real estate units regardless of the deterministic relationship between characteristics that contribute to the formation of the property market price and the same real estate prices. RST was applied to a real estate sample (office units located in Directional District of Naples) and was also integrated with a functional extension so-called Valued Tolerance Relation (VTR) in order to improve its flexibility. A multiple regression analysis (MRA) was developed on the same real estate sample with the aim to compare RST and MRA results. The case study is followed by a brief discussion on basic theoretical connotations of this methodology
Every which way? On predicting tumor evolution using cancer progression models
Successful prediction of the likely paths of tumor progression is valuable for diagnostic,
prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and
thus CPMs encode the paths of tumor progression. Here we analyze the performance of
four CPMs to examine whether they can be used to predict the true distribution of paths of
tumor progression and to estimate evolutionary unpredictability. Employing simulations we
show that if fitness landscapes are single peaked (have a single fitness maximum) there is
good agreement between true and predicted distributions of paths of tumor progression
when sample sizes are large, but performance is poor with the currently common much
smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all
cases, detection regime (when tumors are sampled) is a key determinant of performance.
Estimates of evolutionary unpredictability from the best performing CPM, among the four
examined, tend to overestimate the true unpredictability and the bias is affected by detection
regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability
for several of the data sets. But most of the predictions of paths of tumor progression are
very unreliable, and unreliability increases with the number of features analyzed. Our results
indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and
emphasize the need for methodological work that can account for the probably multi-peaked
fitness landscapes in cancerWork partially supported by BFU2015-
67302-R (MINECO/FEDER, EU) to RDU. CV
supported by PEJD-2016-BMD-2116 from
Comunidad de Madrid to RD
Rough set methodology in meta-analysis - a comparative and exploratory analysis
We study the applicability of the pattern recognition methodology "rough set data analysis" (RSDA) in the field of meta analysis. We give a summary of the mathematical and statistical background and then proceed to an application of the theory to a meta analysis of empirical studies dealing with the deterrent effect introduced by Becker and Ehrlich. Results are compared with a previously devised meta regression analysis. We find that the RSDA can be used to discover information overlooked by other methods, to preprocess the data for further studying and to strengthen results previously found by other methods.Rough Data Set, RSDA, Meta Analysis, Data Mining, Pattern Recognition, Deterrence, Criminometrics
Inferring the parallax of Westerlund 1 from Gaia DR2
Westerlund 1 (Wd1) is potentially the largest star cluster in the Galaxy.
That designation critically depends upon the distance to the cluster, yet the
cluster is highly obscured, making luminosity-based distance estimates
difficult. Using {\it Gaia} Data Release 2 (DR2) parallaxes and Bayesian
inference, we infer a parallax of mas corresponding to a
distance of kpc. To leverage the combined statistics of all
stars in the direction of Wd1, we derive the Bayesian model for a cluster of
stars hidden among Galactic field stars; this model includes the parallax
zero-point. Previous estimates for the distance to Wd1 ranged from 1.0 to 5.5
kpc, although values around 5 kpc have usually been adopted. The {\it Gaia} DR2
parallaxes reduce the uncertainty from a factor of 3 to 18\% and rules out the
most often quoted value of 5 kpc with 99\% confidence. This new distance allows
for more accurate mass and age determinations for the stars in Wd1. For
example, the previously inferred initial mass at the main-sequence turn-off was
around 40 M; the new {\it Gaia} DR2 distance shifts this down to
about 22 M. This has important implications for our understanding of
the late stages of stellar evolution, including the initial mass of the
magnetar and the LBV in Wd1. Similarly, the new distance suggests that the
total cluster mass is about four times lower than previously calculated.Comment: 14 pages, 10 figure
Stochastic analysis of different rough surfaces
This paper shows in detail the application of a new stochastic approach for
the characterization of surface height profiles, which is based on the theory
of Markov processes. With this analysis we achieve a characterization of the
scale dependent complexity of surface roughness by means of a Fokker-Planck or
Langevin equation, providing the complete stochastic information of multiscale
joint probabilities. The method is applied to several surfaces with different
properties, for the purpose of showing the utility of this method in more
details. In particular we show the evidence of Markov properties, and we
estimate the parameters of the Fokker-Planck equation by pure, parameter-free
data analysis. The resulting Fokker-Planck equations are verified by numerical
reconstruction of conditional probability density functions. The results are
compared with those from the analysis of multi-affine and extended multi-affine
scaling properties which is often used for surface topographies. The different
surface structures analysed here show in details advantages and disadvantages
of these methods.Comment: Minor text changes to be identical with the published versio
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