Variable Selection and Functional Form Uncertainty in Cross-Country Growth Regressions

Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these problems independently, yet a joint treatment is essential. We perform this joint treatment by extending the linear model to allow for multiple-regime parameter heterogeneity of the type suggested by new growth theory, while addressing the variable selection problem by means of Bayesian model averaging. Controlling for variable selection uncertainty, we confirm the evidence in favor of new growth theory presented in several earlier studies. However, controlling for functional form uncertainty, we find that the effects of many of the explanatory variables identified in the literature are not robust across countries and variable selections

Essays in Likelihood-Based Computational Econometrics

The theory of probabilities is basically only common sense reduced to a calculus. Pierre Simon Laplace, 1812 The quote above is from Pierre Simon Laplace’s introduction to his seminal work Th´eorie analytique des probabilit´es, in which he lays the groundwork for what is currently known as Bayesian analysis. He proceeds to describe probability theory, and statistical inference, as a method that makes one estimate accurately what right-minded people feel by a sort of instinct, often without being able to give a reason for it. (translation from French: Dale, 1995) This statement contains a profound truth and insight: Probability theory offers a clean and simple recipe for reasoning under uncertainty which I experienced as eyeopening when I first learned about it. As my knowledge of probability theory increased, however, I also realized that in isolation this quote presents things to be much simpler than they actually are: Reducing common sense to a calculus is extremely difficult to do well in practice. Translating our common sense into the language of probabilities takes a lot of practice, and if done accurately it often leads to a calculus without any exact solutions. It is therefore the task of statisticians and econometricians to find practical ways of reducing our common sense to calculus, and to devise smart new methods for efficiently doing the resulting calculations. This work represents my contribution towards these goals

Community detection‐based deep neural network architectures: A fully automated framework based on Likert‐scale data.

Deep neural networks (DNNs) have emerged as a state‐of‐the‐art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio‐demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure

Android malware detection through generative adversarial networks

© 2019 John Wiley & Sons, Ltd. Mobile and cell devices have empowered end users to tweak their cell phones more than ever and introduce applications just as we used to with personal computers. Android likewise portrays an uprise in mobile devices and personal digital assistants. It is an open-source versatile platform fueling incalculable hardware units, tablets, televisions, auto amusement frameworks, digital boxes, and so forth. In a generally shorter life cycle, Android also has additionally experienced a mammoth development in application malware. In this context, a toweringly large measure of strategies has been proposed in theory for the examination and detection of these harmful applications for the Android platform. These strategies attempt to both statically reverse engineer the application and elicit meaningful information as features manually or dynamically endeavor to quantify the runtime behavior of the application to identify malevolence. The overgrowing nature of Android malware has enormously debilitated the support of protective measures, which leaves the platforms such as Android feeble for novel and mysterious malware. Machine learning is being utilized for malware diagnosis in mobile phones as a common practice and in Android distinctively. It is important to specify here that these systems, however, utilize and adapt the learning-based techniques, yet the overhead of hand-created features limits ease of use of such methods in reality by an end user. As a solution to this issue, we mean to make utilization of deep learning–based algorithms as the fundamental arrangement for malware examination on Android. Deep learning turns up as another way of research that has bid the scientific community in the fields of vision, speech, and natural language processing. Of late, models set up on deep convolution networks outmatched techniques utilizing handmade descriptive features at various undertakings. Likewise, our proposed technique to cater malware detection is by design a deep learning model making use of generative adversarial networks, which is responsible to detect the Android malware via famous two-player game theory for a rock-paper-scissor problem. We have used three state-of-the-art datasets and augmented a large-scale dataset of opcodes extracted from the Android Package Kit bytecode and used in our experiments. Our technique achieves F1 score of 99% with a receiver operating characteristic of 99% on the bytecode dataset. This proves the usefulness of our technique and that it can generally be adopted in real life

Human parvovirus B19 infection and hydrops fetalis in Rio de Janeiro, Brazil

Formalin-fixed paraffin embedded lung and liver tissue from 23 cases of non immune hydrops fetalis and five control cases, in which hydrops were due to syphilis (3) and genetic causes (2), were examined for the presence of human parvovirus B19 by DNA hybridisation. Using in situ hybridisation with a biotynilated probe one positive case was detected. Using 32P-labelled probes in a dot blot assay format, five further positives were obtained. These were all confirmed as positive by a nested polymerase chain reaction assay. Electron microscopy revealed virus in all these five positive cases. The six B19 DNA positive cases of hydrops fetalis were from 1974, 1980, 1982, 1987 and 1988, four of which occurred during the second half of the year, confirming the seasonality of the disease