MS²PIP : fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

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IMPLEMENTASI OHLSON’S MODEL SEBAGAI EARLY WARNING SYSTEM DALAM MEMPREDIKSI FINANCIAL DISTRESS NASABAH KREDIT MODAL KERJA DI BRI

Debitur yang memiliki kemampuan untuk mengembalikan kredit tepat waktu dan jumlah yang sesuai merupakan salah satu aset berharga sebuah bank. Kredit bermasalah merupakan salah satu hal yang sangat rentan terjadi di BRI cabang Pahlawan. Oleh karena itu, diperlukan alat yang bisa mendeteksi gejala kebangkrutan sejak dini. Salah satu alat alternatif yang bisa digunakam sebagai pelengkap dari credit risk rating adalah Ohlson’s model. Tujuan dari penelitian ini adalah untuk melihat apakah teori model Ohlson mampumendeteksi financial distress debitur pinjaman di BRI cabang Pahlawan dan variabel-variabel yang turut memprediksi potensi kebangkrutan dalam proses renewal kredit tersebut.Peneliti menggunakan teknik purposive sampling untuk nasabah kredit modal kerja BRI Pahlawan periode 2008-2010. Variabel independen yang digunakan adalah SIZE, TLTA,WCTA, CLCA, OENEG, NITA. FUTL dan INTWO. Data per model dianalisa menggunakan analisa regresi logistik. Hasil analisa regresi logistik menunjukkan bahwa teori model Ohlson dapat mendeteksi financial distress pada debitur pinjaman KMK BRI Pahlawan. Akurasi masing-masing model adalah 93.3% pada model 1, 83.3% pada model 2 dan 73.3% pada model 3. Variabel SIZE adalah variabel yang berpengaruh di semua model. Kata kunci : gejala kebangkrutan, kredit, ohlson’s model, regresi logisti

Dynamics of the Young Binary LMC Cluster NGC 1850

In this paper we have examined the age and internal dynamics of the young binary LMC cluster NGC 1850 using BV CCD images and echelle spectra of 52 supergiants. Isochrone fits to a BV color-magnitude diagram revealed that the primary cluster has an age of $\tau = 90 \pm 30$ Myr while the secondary member has $\tau = 6 \pm 5$ Myr. BV surface brightness profiles were constructed out to R $>$ 40 pc, and single-component King-Michie (KM) models were applied. The total cluster luminosity varied from L$_B$ = 2.60 - 2.65 $\times 10^6$ L$_B$\sol\ and L$_V$ = 1.25 - 1.35 $\times 10^6$ as the anisotropy radius varied from infinity to three times the scale radius with the isotropic models providing the best agreement with the data. Of the 52 stars with echelle spectra, a subset of 36 were used to study the cluster dynamics. The KM radial velocity distributions were fitted to these velocities yielding total cluster masses of 5.4 - 5.9 $\pm 2.4 \times 10^4$ M\sol\ corresponding to M/L$_B$ = 0.02 $\pm 0.01$ M\sol/L$_B$\sol\ or M/L$_V$ = 0.05 $\pm 0.02$ M\sol/L$_V$\sol. A rotational signal in the radial velocities has been detected at the 93\% confidence level implying a rotation axis at a position angle of 100\deg. A variety of rotating models were fit to the velocity data assuming cluster ellipticities of $\epsilon = 0.1 - 0.3$. These models provided slightly better agreement with the radial velocity data than the KM models and had masses that were systematically lower by a few percent. The preferred value for the slope of a power-law IMF is a relatively shallow, x = 0.29 \pmm{+0.3}{-0.8} assuming the B-band M/L or x = 0.71 \pmm{+0.2}{-0.4} for the V-band.Comment: 41 pages (figures available via anonymous FTP as described below

Exclusive dealing as a barrier to entry? Evidence from automobiles.

Exclusive dealing contracts between manufacturers and retailers force new entrants to set up their own costly dealer networks to enter the market. We ask whether such contracts may act as an entry barrier, and provide an empirical analysis of the European car market. We first estimate a demand model with product and spatial differentiation, and quantify the role of a dense distribution network in explaining the car manufacturers’ market shares. We then perform policy counterfactuals to assess the pro.t incentives and entry-deterring effects of exclusive dealing. We find that there are no individual incentives to maintain exclusive dealing, but there can be a collective incentive by the industry as a whole, even absent efficiencies. Furthermore, a ban on exclusive dealing would shift market shares from the larger European firms to the smaller entrants. More importantly, consumers would gain substantially, mainly because of the increased spatial availability and less so because of intensified price competition. Our findings suggest that the European Commission’s recent decision to facilitate exclusive dealing in the car market may not have been warranted.

Novelty Detection in Sequential Data by Informed Clustering and Modeling

Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can be detected by modeling normal sequences and measuring the deviations of a new sequence from the model predictions. However, in many applications data is generated by several distinct processes so that models trained on all the data tend to over-generalize and novelties remain undetected. We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately. However, this comes at a trade-off, since the amount of training data per cluster is reduced. This is a particular problem for discrete sequences where state-of-the-art models are data-hungry. The success of this approach thus depends on the quality of the clustering, i.e., whether the individual learning problems are sufficiently simpler than the joint problem. While clustering discrete sequences automatically is a challenging and domain-specific task, it is often easy for human domain experts, given the right tools. In this paper, we adapt a state-of-the-art visual analytics tool for discrete sequence clustering to obtain informed clusters from domain experts and use LSTMs to model each cluster individually. Our extensive empirical evaluation indicates that this informed clustering outperforms automatic ones and that our approach outperforms state-of-the-art novelty detection methods for discrete sequences in three real-world application scenarios. In particular, decomposition outperforms a global model despite less training data on each individual cluster