124,662 research outputs found
Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis
No full textIn professional tennis, it is often acknowledged that the server has an initial advantage.
Indeed, the majority of points are won by the server, making the serve one of the most important
elements in this sport. In this paper, we focus on the role of the serve advantage in winning a
point as a function of the rally length. We propose a Bayesian isotonic logistic regression model
for the probability of winning a point on serve. In particular, we decompose the logit of the
probability of winning via a linear combination of B-splines basis functions, with athlete-specific
basis function coefficients. Further, we ensure the serve advantage decreases with rally length
by imposing constraints on the spline coefficients. We also consider the rally ability of each
player, and study how the different types of court may impact on the player’s rally ability. We
apply our methodology to a Grand Slam singles matches datase
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
In-plane frictional resistances in dry block masonry walls and rocking-sliding failure modes revisited and experimentally validated
No full textThis paper presents new findings in the assessment of the lateral strength of dry block masonry walls under in-plane loading, based on an existing macro-modelling approach using limit analysis methods. The evaluation of the in-plane frictional resistances activated at the onset of the rocking-sliding mechanisms is revisited and two equivalent formulations accounting for the self weight of the wall and additional loads are presented. The accuracy and robustness of the analytical results are assessed by experimentally testing both the resultant frictional resistances and their applications points. The solution procedure of the previous macro-block model providing upper and lower bounds for the ultimate load factor is also reconsidered and the computation of the “exact” load factor falling within the range is proposed. A satisfactory comparison is found with a micro-block and other macro-block models existing in the literature. This comparison is carried out through a parametric analysis, in terms of both the load factor and the failure mode and with reference to the effects of chosen parameters (wall aspect, unit aspect, unit size ratios and overload) on the load factor
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Gaussian graphical modeling for spectrometric data analysis
No full textMotivated by the analysis of spectrometric data, a Gaussian graphical model for learning the dependence structure among frequency bands of the infrared absorbance spectrum is introduced. The spectra are modeled as continuous functional data through a B-spline basis expansion and a Gaussian graphical model is assumed as a prior specification for the smoothing coefficients to induce sparsity in their precision matrix. Bayesian inference is carried out to simultaneously smooth the curves and to estimate the conditional independence structure between portions of the functional domain. The proposed model is applied to the analysis of infrared absorbance spectra of strawberry purees
Functional Graphical Model for Spectrometric Data Analysis
No full textMotivati dall’analisi di dati spettroscopici, introduciamo un modello grafico funzionale per l’apprendimento della struttura di indipendenza condizionale degli spettri. Gli spettri di assorbimento sono modellati come dati funzionali continui attraverso una espansione in base B-spline cubica. Un modello grafico gaussiano è utilizzato per i coefficienti dell’espansione di base, attraverso il quale viene indotta una struttura sparsa per la matrice di precisione dei coefficienti. L’inferenza bayesiana del modello permette di ottenere una stima della struttura di indipendenza condizionale tra le bande di frequenza degli spettri. Il modello proposto è applicato all’analisi degli spettri di assorbimento infrarosso di puree di fragole.Motivated by the analysis of spectrographic data, we introduce a functional graphical model for learning the conditional independence structure of spectra. Absorbance spectra are modeled as continuous functional data through a cubic B-spline basis expansion. A Gaussian graphical model is assumed for basis expansion coefficients, where a sparse structure is induced for the precision matrix. Bayesian inference is carried out, providing an estimate of the precision matrix of the coefficients, which translates into an estimate of the conditional independence structure between frequency bands of the spectrum. The proposed model is applied to the analysis of the infrared absorbance spectra of strawberry purees
Learning Block Structured Graphs in Gaussian Graphical Models
No full textA prior distribution for the underlying graph is introduced in the framework of Gaussian graphical models. Such a prior distribution induces a block structure in the graph's adjacency matrix, allowing learning relationships between fixed groups of variables. A novel sampling strategy named Double Reversible Jumps Markov chain Monte Carlo is developed for learning block structured graphs under the conjugate G-Wishart prior. The algorithm proposes moves that add or remove not just a single edge of the graph but an entire group of edges. The method is then applied to smooth functional data. The classical smoothing procedure is improved by placing a graphical model on the basis expansion coefficients, providing an estimate of their conditional dependence structure. Since the elements of a B-Spline basis have compact support, the conditional dependence structure is reflected on well-defined portions of the domain. A known partition of the functional domain is exploited to investigate relationships among portions of the domain and improve the interpretability of the results. for this article are available online
Learning to signal: Analysis of a micro-level reinforcement model
No full textAbstractWe consider the following signaling game. Nature plays first from the set {1,2}. Player 1 (the Sender) sees this and plays from the set {A,B}. Player 2 (the Receiver) sees only Player 1’s play and plays from the set {1,2}. Both players win if Player 2’s play equals Nature’s play and lose otherwise. Players are told whether they have won or lost, and the game is repeated. An urn scheme for learning coordination in this game is as follows. Each node of the decision tree for Players 1 and 2 contains an urn with balls of two colors for the two possible decisions. Players make decisions by drawing from the appropriate urns. After a win, each ball that was drawn is reinforced by adding another of the same color to the urn. A number of equilibria are possible for this game other than the optimal ones. However, we show that the urn scheme achieves asymptotically optimal coordination
Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis
No full textIn professional tennis, it is often acknowledged that the server has an initial advantage. Indeed, the majority of points are won by the server, making the serve one of the most important elements in this sport. In this paper, we focus on the role of the serve advantage in winning a point as a function of the rally length. We propose a Bayesian isotonic logistic regression model for the probability of winning a point on serve. In particular, we decompose the logit of the probability of winning via a linear combination of B-splines basis functions, with athlete-specific basis function coefficients. Further, we ensure the serve advantage decreases with rally length by imposing constraints on the spline coefficients. We also consider the rally ability of each player, and study how the different types of court may impact on the player’s rally ability. We apply our methodology to a Grand Slam singles matches dataset
Seismic behaviour of a mixed iron-masonry church: Santa Maria Maddalena, Ischia
No full textThe concept of vulnerability of the existing building stock is receiving increasing awareness and central importance in the scientific community working in earthquake risk mitigation. This assumes even more relevance when dealing with heritage structures located in relevant seismic hazard zones. This paper aims to identify and describe the earthquake-resistant features found in a unique masonry church in Ischia (Italy), and discuss their effectiveness on the impact of the post-seismic damage through the application of non-linear static analyses. The Santa Maria Maddalena Church represents one of the rare examples in which the technology of the Borbonic casa Baraccata (mixed timber-masonry construction), already well-known in the literature for its use in Italian seismic areas since the eighteenth century, is applied with the non-conventional combination of masonry and iron frames. The church was struck by the recent earthquake of 21 August 2017 with epicentre in Casamicciola Terme. The recorded post-seismic damage of the church evidenced non-relevant structural crack patterns, which are likely to be related to the efficacy of the construction system adopted
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