720 research outputs found
Markov or not Markov - this should be a question
No full textAlthough it is well known that Markov process theory, frequently applied in the literature on income convergence, imposes some very restrictive assumptions upon the data generating process, these assumptions have generally been taken for granted so far. The present paper proposes, resp. recalls chi-square tests of the Markov property, of spatial independence, and of homogeneity across time and space to assess the reliability of estimated Markov transition matrices. As an illustration we show that the evolution of the income distribution across the 48 coterminous U.S. states from 1929 to 2000 clearly has not followed a Markov process.
Existence, uniqueness and a constructive solution algorithm for a class of finite Markov moment problems
No full textWe consider a class of finite Markov moment problems with arbitrary number of positive and negative branches. We show criteria for the existence and uniqueness of solutions, and we characterize in detail the non-unique solution families. Moreover, we present a constructive algorithm to solve the moment problems numerically and prove that the algorithm computes the right solution.Inverse problems, finite Markov moment problem, exponential transform.
Nouvelle méthode syntagmatique de vectorisation appliquée au self-organizing map des textes vietnamiens
No full text@inproceedings{CN-NGUYEN-2004, author = {Nguyen D.T.}, title = {Nouvelle méthode syntagmatique de vectorisation appliquée au self-organizing map des textes vietnamiens}, booktitle = {RECIRAL'04}, year = {2004}, address = {Fès, Maroc}, month = {avril} }National audienc
Markov Chain approach to Purchasing Power Convergence in the 15 European Union
No full textIn the present paper we study the degree of convergence in the European Union from the Purchasing Power Parity (PPP) point of view. The price of the shopping basket can be the cause of disparities in a global market in construction that, like the European Union, is formed by different countries with different consumption habits. In addition, in this construction process twelve out of fifteen countries of the EU have left its national currency to adopt the Euro like common currency. Therefore, it is necessary for the stability of the Union process in the long run that, among others, purchasing power of the different state members tends towards a same common value. Moreover, the question is whether that process of convergence within the European Union is taking place or not. In order to solve this question, the series of the Absolute Purchasing Power Parity (APPP) are estimated through the suggestion of Rodriguez et al (2004). These authors use the Harmonized Consumer Price Index in the European Union and the nominal exchange rates of the different currencies with euro. Monthly estimates of the APPP series for the 1995-2002 period are obtained for each of the fifteen countries. These figures show, for each country, their relative position to the average value of the European Union. Using these series we applied the Markov Chain methodology to study the time evolution of the distribution of APPP in the European Union. This methodology has been very used by its facility of calculation and interpretation of the results. Nevertheless, with the purpose of obtaining good estimations it is necessary to solve the discretization problem of a continuous variable. This is, to use a finite set, and relatively small number of states, for a variable with infinite values. In the present work different approaches are used to solve the problem. We test for structural change on the estimated probabilities using adapted test to Markov Chains. This allows us to study if an effect exists on the Purchasing Power Parity with the entrance of the Euro. Markov Chains are estimated by Maximum likelihood, and allow us to do different analyses. In the first place, we can study the mobility of the distribution, measured through the probabilities of permanence or not in the same state, and in the degree of diagonal structure of the resulting matrix. This objective can obtained by direct observation, calculating Mobility Index, or using expected time of first passage. Secondly, we can obtain the ergodic or long term distribution. This one shows the temporary evolution in the long run of the distribution, under the hypothesis of maintenance of the present conditions. This distribution would show the possible convergence or not of the whole distribution. We also estimate elasticities of ergodic probabilities, to analyze the effect of each probability in the Markov chain in the long run distribution. Results show differences with the Euro Entry, mobility towards convergence within the distribution is slow, with high elasticities of the ergodic distribution to changes in the transition probabilities.
Mapping the Landscape: A Bibliometric Analysis of CALIBER 2022 Convention Publications
No full textThe present study examines the authorship patterns, collaboration levels, and various other parameters such as gender, author designation, institutional affiliation, and geographical distribution of the conference papers presented at CALIBER 2022 by employing an array of bibliographic analysis techniques. The analysis is based on a dataset consisting of 45 papers authored by 100 individuals and found that authors hailing from Uttar Pradesh emerged as the foremost contributors. The study also found that universities emerged as the most prolific contributors, responsible for the publication of 71.00 per cent of the articles within the designated time frame
Convergence Across Provinces of Turkey : A Spatial Analysis
No full textThe aim of this study is to analyze regional disparities and to test the convergence hypothesis across the provinces in Turkey. The study also attempts to analyze the spatial spillovers in the growth process of the provinces. The analyses cover the 1987-2001 period. Two alternative methodologies are used in the analyses. First, the methodology of b-convergence based on cross-sectional regressions is used and the effects of spatial dependence are analyzed by using spatial econometric techniques. Second, Markov chain analysis is employed and spatial dependence is integrated using spatial Markov chains. Results from both methodologies signal non-existence of convergence and the existence of spatial spillovers in the growth process of provinces.Regional Disparities, b-convergence, Markov Chains, Spatial Econometrics.
Does Foreign Direct Investment Promote Regional Development in Developed Countries? A Markov Chain Approach for US States.
No full textDirektinvestition; Regionale Entwicklung; Sozialprodukt; Regionale Disparität; Schätzung; USA;Markov transition probability , likelihood ratio test , FDI , per-capita income , regional development , United States of America;
Stochasticc convection parameterization
No full textClouds are chaotic, difficult to predict, but above all, magnificent natural phenomena. There are different types of clouds: stratus, a layer of clouds that may produce drizzle, cirrus, clouds in the higher parts of the atmosphere, and cumulus, clouds that arise in convective updrafts. Thermals, rising air that is often used by birds and gliders to gain height, are an example of atmospheric convection. When the sun heats Earth’s surface layer, warm and moist air rises in thermals to higher parts of the atmosphere. In this way, convection transports heat and moisture vertically in the atmosphere. This often leads to the formation of clouds and heavy rainfall. A major part of the rainfall on Earth, especially in the tropics, is produced by cumulus clouds. Furthermore, convection and cloud formation affect the large-scale planetary circulation. In the atmosphere, these processes are of major importance for Earth’s weather and climate. Convection and clouds also play a major role in numerical simulations of weather and climate. With general circulation models, the large-scale wind circulation and variables such as temperature and humidity are calculated on a three-dimensional global grid. The model grid resolution is low, and therefore, smaller-scale processes such as convection and cloud formation can not be calculated explicitly. The impact of these small-scale processes has to be determined in another way. They are represented by parameterizations that give an estimate of the effect of the smallscale processes on the large-scale model variables. For models with relatively large columns, the presence of a large number of realizations of the same small-scale process justifies the expression of their effect on the large-scale variables in terms of statistical properties. For example, the effect of a large number of clouds can be represented statistically. A problem arises from the fact that the resolution of operational weather and climate models tends to increase. Generally speaking, with higher model resolutions the atmosphere can be simulated more accurately. However, if resolutions keep increasing, the expression of the small-scale effects in terms of statistical properties can no longer be justified. In a small model column, there is for example only space for a small number of clouds. The chaotic behavior of convective clouds becomes an important factor and deterministic parameterizations no longer give accurate estimates. The increase of fluctuations and randomness is a motivation for using stochastic convection parameterizations. The central research theme in this dissertation is stochastic convection parameterization. Stochastic processes are used in the representation of convective clouds. Traditional deterministic parameterizations only give an estimate of the expected value of the effect of small-scale variables. Stochastic parameterizations can deviate from this expected value and can produce a range of convective responses. Especially in models with a relatively high resolution, it is important that parameterizations can represent fluctuations around the expected value. There are several ways of introducing stochastics. In this dissertation, Markov chains are examined, stochastic processes that are named after the famous Russian mathematician Andrei Markov (1856-1922). Markov chains have a finite number of states of which the transition probabilities can be estimated from data. By inferring transition probabilities from high-resolution data of convection, Markov chains mimic convective behavior.A Large-Eddy Simulation model is used to construct a data set. Large-Eddy Simulation models are able to resolve clouds and convection in detail. After inference of the Markov chains, they are able to mimic clouds and convection as observed in a field-experiment near Barbados. The same method has also been applied for convective clouds in Brazil. These Markov chains only work for a very specific range of atmospheric circumstances. Therefore, another Markov chain model is constructed from a large observational data set from a rain radar in Darwin, Australia. A larger range of atmospheric circumstances is covered, and the Markov chains can be applied more generally. The Darwin Markov chains are implemented in a climate model to stochastically parameterize convection. This improves the variability related to convection as well as the distribution of the simulated tropical precipitation. The Markov-chain model is not perfect yet; however, a large step has been made in the development of this stochastic method for usage in state-of-the-art weather and climate models
Stochastic parameterization of convective area fractions with a multicloud model inferred from observational data
No full textObservational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convection parameterization schemes used in general circulation models.Geoscience and Remote SensingCivil Engineering and Geoscience
Eastern Iran in the Achaemenid Period
No full textThe author deals with the archaeological evidence of the Achaemenid period in eastern Iran. This evidence is limited, rare and contradictory with regard to the historical importance of the eastern provinces of the Empire. The territorial extent of the Achaemenid Empire is ambiguous too and in this regard the cultural background of the different provinces, as well as relationships between center and periphery, were crucial factors affecting the visibility of the Achaemenid empire in its eastern-most regions. Similarly, the geographic definition of ‘eastern Iran’ requires clarification as well because, as a geomorphological unit. Thus at least four different aspects of interpretation should be considered when considering the evidence of the Achaemenid empire in the east:
1. the dynastic - identifiable by inscriptions, coins and seals
2. the ethnic - possibly detectable on both physical anthropological and cultural grounds
3. the political/imperial - recognizable both in macroscopic architectural and art historical remains and in the material traces of settlement patterns and economic investments, e.g. to secure the water supply
4. the chronological - interpretable in the differing horizons connected to the period of Achaemenid political-dynastic dominion in the area
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