Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model Machine Learning

In econometrics and finance, the vector error correction model (VECM) is an important time series model for cointegration analysis, which is used to estimate the long-run equilibrium variable relationships. The traditional analysis and estimation methodologies assume the underlying Gaussian distribution but, in practice, heavy-tailed data and outliers can lead to the inapplicability of these methods. In this paper, we propose a robust model estimation method based on the Cauchy distribution to tackle this issue. In addition, sparse cointegration relations are considered to realize feature selection and dimension reduction. An efficient algorithm based on the majorization-minimization (MM) method is applied to solve the proposed nonconvex problem. The performance of this algorithm is shown through numerical simulations.

Designing communication systems via iterative improvement: error correction coding with Bayes decoder and codebook optimized for source symbol error Artificial Intelligence

In error correction coding (ECC), the typical error metric is the bit error rate (BER) which measures the number of bit errors. For this metric, the positions of the bits are not relevant to the decoding, and in many noise models, not relevant to the BER either. In many applications this is unsatisfactory as typically all bits are not equal and have different significance. We look at ECC from a Bayesian perspective and introduce Bayes estimators with general loss functions to take into account the bit significance. We propose ECC schemes that optimize this error metric. As the problem is highly nonlinear, traditional ECC construction techniques are not applicable and we use iterative improvement search techniques to find good codebooks. We provide numerical experiments to show that they can be superior to classical linear block codes such as Hamming codes and decoding methods such as minimum distance decoding.

Software for Data Mining, Analytics,Data Science, and Knowledge Discovery

AITopics Original Links

Classification software: building models to separate 2 or more discrete classes using Multiple methods Decision Tree Rules Neural Bayesian SVM Genetic, Rough Sets, Fuzzy Logic and other approaches Analysis of results, ROC Social Network Analysis, Link Analysis, and Visualization software Text Analysis, Text Mining, and Information Retrieval (IR) Web Analytics and Social Media Analytics software. BI (Business Intelligence), Database and OLAP software Data Transformation, Data Cleaning, Data Cleansing Libraries, Components and Developer Kits for creating embedded data mining applications Web Content Mining, web scraping, screen scraping.

Internet of Things and Bayesian Networks


As big data becomes more of cliche with every passing day, do you feel Internet of Things is the next marketing buzzword to grapple our lives. So what exactly is Internet of Thing (IoT) and why are we going to hear more about it in the coming days. Internet of thing (IoT) today denotes advanced connectivity of devices,systems and services that goes beyond machine to machine communications and covers a wide variety of domains and applications specifically in the manufacturing and power, oil and gas utilities. An application in IoT can be an automobile that has built in sensors to alert the driver when the tyre pressure is low. Built-in sensors on equipment's present in the power plant which transmit real time data and thereby enable to better transmission planning,load balancing.