Clustering Via Nonparametric Density Estimation: the R Package pdfCluster

The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. After summarizing the main aspects of the methodology, we describe the features and the usage of the package, and finally illustrate its working with the aid of two datasets

Nonparametric clustering for image segmentation

open1openMenardi, GiovannaMenardi, Giovann

PENGARUH STRUKTUR AKTIVA, UKURAN PERUSAHAAN DAN PROFITABILITAS TERHADAP STRUKTUR MODAL PADA PERUSAHAAN KONSTRUKSI BANGUNAN YANG TERDAFTAR DI BURSA EFEK INDONESIA

Struktur modal merupakan pembelanjaan jangka panjang perusahaan yang diperoleh dari perbandingan hutang jangka panjang terhadap modal sendiri. Dalam penentuan sumber pendanaan yang akan digunakan, serta berapa besarnya proporsi masing-masing sumber dana tersebut maka perusahaan akan menganalisis beberapa faktor untuk mencapai struktur modal yang optimal. Penelitian ini bertujuan untuk menguji dan mengetahui pengaruh dari struktur aktiva, ukuran perusahaan, dan profitabilitas terhadap struktur modal Perusahaan Konstruksi Bangunan di Bursa Efek Indonesia periode tahun 2011-2015. Teknik pengambilan sampel menggunakan Purposive Sampling Method. Sampel yang digunakan dalam penelitian ini sebanyak 7 perusahaan konstruksi bangunan yang terdaftar di Bursa Efek Indonesia yaitu Adhi Karya (Persero) Tbk, Nusa Konstruksi Enjiniring Tbk, Pembangunan Perumahan (Persero) Tbk, Surya Semesta Internusa Tbk, Total Bangun Persada Tbk, Wijaya Karya (Persero) Tbk, dan Waskita Karya (Persero) Tbk. Hasil penelitian ini menunjukkan bahwa struktur aktiva dan profitabilitas berpengaruh signifikan terhadap struktur modal. Sedangkan ukuran perusahaan tidak signifikan terhadap struktur modal. Kata kunci: struktur modal, struktur aktiva, ukuran perusahaan dan profitabilita

On the stability of performance measures over time.

Performance persistence is a relevant issue when evaluating the predictability of future results of managed portfolios. A related crucial aspect is the stability over time of the measure used to assess the performance, defined as the degree of association between the rankings of financial assets induced by the performance measure throughout subsequents periods. In this work a general class of possible criteria to measure stability is proposed. Then, the attention is focused on a specific index, whose asymptotic expected value and variance are derived under the null hypothesis of absence of stability. Furthermore, two statistical tests for evaluating the significance of stability are discussed. An application to a large set of US equity mutual funds shows that stability may remarkably vary, as the performance measure or the time widow width where it is computed change

Double clustering for rating mutual funds

Due to the increasing proliferation of mutual funds, in-depth evaluationof the available products for portfolio selection purposes is a difficult task.Hence, classification schemes giving quick information about which fundsare worth to be monitored, are often provided. The aim of this work is toshow an application of clustering methods to the mutual funds historicaldata. Starting from the monthly time series of the Net Asset Values of aspecific style-based category, namely the Large Blend US mutual funds, weapply distance-based clustering methods twice on a set of return, risk andperformance measures: firstly, with the aim of reducing data dimension, andsecondly to cluster funds in homogeneous classes. The adopted procedureclaims the feature of producing a partition of funds that are readily inter-pretable from a financial point of view and it is further possible to rank theidentified groups, thus obtaining a rating of funds that turns out to accountfor different propensities toward the risk exposure

The "Niola Doa-like" motifs in the Gran Riparo d'Archeï (Ennedi, Chad)

The existence in the Gran Riparo d’Archeï of paintings recalling the iconography of the famed Niola Doa petroglyphs was recognised at the time of the first reports, in the 90s. By scanning in 3D with a digital camera the already described evidence for a “Niola Doa culture” in this major site, a few but significant additional details are revealed

Training and assessing classification rules with unbalanced data

The problem of modeling binary responses by using cross-sectional data has been addressed with a number of satisfying solutions that draw on both parametric and nonparametric methods. However, there exist many real situations where one of the two responses (usually the most interesting for the analysis) is rare. It has been largely reported that this class imbalance heavily compromises the process of learning, because the model tends to focus on the prevalent class and to ignore the rare events. However, not only the estimation of the classification model is affected by a skewed distribution of the classes, but also the evaluation of its accuracy is jeopardized, because the scarcity of data leads to poor estimates of the model’s accuracy. In this work, the effects of class imbalance on model training and model assessing are discussed. Moreover, a unified and systematic framework for dealing with both the problems is proposed, based on a smoothed bootstrap re-sampling technique