Estimation of AR and ARMA models by stochastic complexity

In this paper the stochastic complexity criterion is applied to estimation of the order in AR and ARMA models. The power of the criterion for short strings is illustrated by simulations. It requires an integral of the square root of Fisher information, which is done by Monte Carlo technique. The stochastic complexity, which is the negative logarithm of the Normalized Maximum Likelihood universal density function, is given. Also, exact asymptotic formulas for the Fisher information matrix are derived.Comment: Published at http://dx.doi.org/10.1214/074921706000000941 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Managementul dermatomicozelor cu componentă inflamatorie

Infecțiile fungice ale pielii (sau dermatomicozele) sunt asociate cu o gamă largă de agenţi patogeni, implicarea bacteriilor gram-pozitive fiind adesea suspectata în cazul dermatomicozelor. Inflamaţia joacă un rol important în cazul dermatomicozelor, deseori existând o asociere între frecvenţa episoadelor inflamatorii și indicele redus al calităţii vieţii pacienţilor (asociat problemelor tegumentare). Combinația diflucortolon valerat cu nitrat de isoconazol crește biodisponibilitatea locală a isoconazolului (ISN) comparativ cu isoconazol (ISN) în monoterapie. Combinația diflucortolon valerat cu nitrat de isoconazol are un debut mai rapid al acțiunii antimicotice, reducând pruritul și alte simptome inflamatorii, având în general un beneficiu terapeutic și o rată de vindecare micologică îmbunătățită. Combinația diflucortolon valerat cu nitrat de isoconazol este efi cientă și în tratamentul infecţiilor micotice cu componentă inflamatorie, dar și în eradicarea infecţiilor bacteriene cu germeni gram-pozitivi ce pot acompania dermatomicozele. Îmbunătățirea rapidă a simptomatologiei, alături de profilul de siguranță și tolerabilitate favorabile, asigură pacientului un grad de satisfacție superior și, prin urmare, poate fi un instrument eficient pentru a crește aderența la tratament pentru pacienții cu dermatomicoze însoțite de semne și simptome inflamatorii

Giant renal cell carcinoma in a patient with ipsilateral lower limb hypertrophic lichen planus; Case report and literature review

Renal cell carcinoma is the most common type of primary urogenital cancer, usually resistant to radiotherapy and chemotherapy. Hypertrophic lichen planus is an inflammatory dermatosis characterized by the presence of papulosquamous and intensely pruritic lesions. The association of these two conditions is unusual, being reported in the specialized literature only in a few rare cases with the onset of lichenoid lesions after patients have undergone various forms of treatment. The case of a 62-year-old male patient who was admitted for severe abdominal pain due to a giant renal tumor associated with a hypertrophic plaque located on the anterior part of the left calf is presented. After (clinical, biochemical, imaging) diagnosis, surgery was performed for en bloc removal of the entire mass, adrenal gland, and spleen. The histopathological exam established the diagnosis of a moderately differentiated T2b clear cell Grawitz tumor, without regional lymph node metastasis (stage II). The patient continued local corticosteroid therapy in the hospital for hypertrophic lichen planus lesions, being referred to the oncology department after discharge

DIAGNOSTICUL NEVULUI SPITZ RĂMÂNE O PROVOCARE

Introducere. Nevul Spitz este o leziune melanocitară benignă, cel mai frecvent întâlnită la copil şi adultul tânăr, la nivelul capului şi extremităţilor, cu o evoluţie rapidă de ordinul lunilor, ce îngrijorează familia şi medicul. Scop. Evidenţierea difi cultăţilor de diagnosticare clinică, dermoscopică şi histopatologică într-o leziune ce poate simula melanomul. Material şi metodă. Prezentăm două cazuri de nev Spitz apărute la vârsta de 10 şi respectiv 16 ani, o leziune hiperpigmentată, neagră şi o alta nepigmentată, roză, trimise în consult cu suspiciunea de malignitate. Ambele leziuni au fost evaluate dermoscopic şi histopatologic. Rezultate. Diagnosticul fi nal a fost de nev Spitz clasic pentru leziunea nepigmentată şi de nev Spitz atipic (tumoră spitzoidă cu potenţial malign incert) pentru leziunea pigmentată. Concluzii. Diagnosticarea nevului Spitz continuă să fi e o provocare clinică, dermoscopică şi, nu în ultimul rând, histopatologică, iar excizia chirurgicală este recomandată în majoritatea cazurilor

Efficient Algorithms for Discrete Universal Denoising

The paper is focused on the problem of discrete universal denoising: one estimates the input sequence to a discrete channel based on the observation of the entire output signal, and without assuming any particular knowledge on the statistical properties of the input sequence. A 2k +1 sliding window denoiser (DUDE) has recently been introduced, and its asymptotic optimality was proven in the case of memoryless channels and additive channels with memory. However, DUDE is computationally infeasible for large values of its context parameter k. The purpose of this paper is to further investigate DUDE in the case of channels with memory. First, for the important family of binary additive channels, we propose HDUDE, a computationally feasible implementation of DUDE. It modifies the DUDE algorithm to exploit the property of the block transition probability matrix to be diagonalized by the Hadamard transform. H-DUDE accommodates large values of k, and we demonstrate this for the particular case of the finite-memory contagion channel. Second, we apply DUDE for a non-additive channel model that was previously used in design of stack filters to show its favorable performance

Maximum Entropy Expectation-Maximization Algorithm for Fitting Latent-Variable Graphical Models to Multivariate Time Series

This work is focused on latent-variable graphical models for multivariate time series. We show how an algorithm which was originally used for finding zeros in the inverse of the covariance matrix can be generalized such that to identify the sparsity pattern of the inverse of spectral density matrix. When applied to a given time series, the algorithm produces a set of candidate models. Various information theoretic (IT) criteria are employed for deciding the winner. A novel IT criterion, which is tailored to our model selection problem, is introduced. Some options for reducing the computational burden are proposed and tested via numerical examples. We conduct an empirical study in which the algorithm is compared with the state-of-the-art. The results are good, and the major advantage is that the subjective choices made by the user are less important than in the case of other methods

Multivariate Time Series Imputation: An Approach Based on Dictionary Learning

The problem addressed by dictionary learning (DL) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. Both the dictionary and the sparse representations are learned from the data. We show how DL can be employed in the imputation of multivariate time series. We use a structured dictionary, which is comprised of one block for each time series and a common block for all the time series. The size of each block and the sparsity level of the representation are selected by using information theoretic criteria. The objective function used in learning is designed to minimize either the sum of the squared errors or the sum of the magnitudes of the errors. We propose dimensionality reduction techniques for the case of high-dimensional time series. For demonstrating how the new algorithms can be used in practical applications, we conduct a large set of experiments on five real-life data sets. The missing data (MD) are simulated according to various scenarios where both the percentage of MD and the length of the sequences of MD are considered. This allows us to identify the situations in which the novel DL-based methods are superior to the existing methods