Estimating Discrete Markov Models From Various Incomplete Data Schemes

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis-Hastings algorithm is then proposed and empirically studied.Comment: 26 pages - preprint accepted in 20th February 2012 for publication in Computational Statistics and Data Analysis (please cite the journal's paper

Agricultural Production Intensification in Ukraine: Decision Support of Agricultural Policies Based On the Assessment of Ecological and Social Impacts in Rural Areas

Agriculture is one of the major economic sectors of Ukraine. Therefore, improving agricultural practices is of critical importance for economy, environment, and society in Ukraine. Rapidly increasing intensification of agricultural production promotes large agrarian enterprises. These processes lead to certain consequences. An export-oriented business has a raw character, doesn't fulfill its social role and carries environmental risks. The main goal of these studies is to investigate flexible crop production portfolios/practices at the regional and district level in Ukraine to meet local agro-ecological norms, consumers demand, financial resources, availability of infrastructure and help to improve local strategies for food security and robust land resource utilization. The proposed stochastic model calculates in accordance with available database at regional (25 regions) and district (496 districts) levels the results, which are visualized using GIS software. This paper analyzes current land use processes and develops proper policy recommendations to mitigate the negative consequences (socio, ecology, economic) of unsustainable agricultural intensification in the future

Integrated modeling approach to the analysis of food security and sustainable rural developments: Ukrainian case study

In Ukraine, the growth of intensive agricultural enterprises with a focus on fast profits contributes considerably to food insecurity and increasing socio-economic and environmental risks. Ukraine has important natural and labor resources for effective rural development. For example, more than 50% of food production is still managed in small and medium farms despite the difficulties associated with economic instabilities and the lack of proper policy support. The main issue for the agro-policy nowadays is to use these resources in a sustainable way enforcing robust long term development of rural communities and agriculture. In this paper, we introduce a stochastic geographically explicit model for designing forward looking policies regarding robust resources allocation and composition of agricultural production enhancing food security and rural development. In particular, we investigate the role of investments into rural facilities to stabilize and enhance the performance of the agrofood sector in view of uncertainties and incomplete information. The security goals are introduced in the form of multidimensional risk indicators

Analysis of Atmospheric CO2 Concentration Variations by Using Spectral Indexes: Ukrainian case study

The objective of these studies is to analyze the possibility of determining CO2 concentration in the atmosphere by using spectral indexes - Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) and GPP, PsnNet. In the studies, spatially explicit NDVI and EVI values at a resolution of 1km were calculated for two years, 2009 and 2010, and used for the analysis of dependencies between plant biomass growth and environmental conditions . CO2 concentration in the atmosphere, temperature, precipitation, and time of the year. For every spatial point, the value of photosynthesis activity was calculated and the relationships among the CO2 exchanges, the remotely sensed Normalized Difference Vegetation Index (NDVI), and other environmental factors were examined using the Pearson correlation coefficient. These studies explore the relationship between MODIS products, i.e., Normalized Difference Vegetation Index, Enhanced Vegetation Index, gross primary productivity, net Photosynthesis, and CO2 concentration derived from GOSAT satellite. These measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, quotas verification, validation and calibration of carbon flux models and can supplement data base. Also understanding how increasing concentration to improve plant growth can help to calculate biomass potential in CO2 accumulation

Food Security and Socioeconomic Aspects of Sustainable Rural Development in Ukraine

In this paper we analyze current agriculture development trends in Ukraine. Using the results of the analysis, collected data and experts estimates we develop an integrated approach to assist decision making regarding long-term and robust agricultural policies that ensure scio-economic goals, food security and environmental safety. The proposed stochastic geographically explicit model for the analysis of robust rural development strategies adopts different criteria, among others are satisfying local demands consistent with the country-wide food production targets. The paper discusses application of the model with selected results on the level of Ukrainian oblasts

Risk, Security and Robust Solutions

The aim of this paper is to develop a decision-theoretic approach to security management of uncertain multi-agent systems. Security is defined as the ability to deal with intentional and unintentional threats generated by agents. The main concern of the paper is the protection of public goods from these threats allowing explicit treatment of inherent uncertainties and robust security management solutions. The paper shows that robust solutions can be properly designed by new stochastic optimization tools applicable for multicriteria problems with uncertain probability distributions and multivariate extreme events

Оптимізаційні питання оцінювання щільності на реальних даних

Для оцінювання щільності статистичного розподілу часто застосовують підхід максимальної ентропії, рівносильний підходу максимальної правдоподібності. Однак на малих наборах вхідних даних такий підхід дає надлишковість оцінки. Надлишковість оцінки можна усувати такими методами згладження як регуляризація чи переформулювання обмежень.The maximum entropy approach, equivalent to the maximum likelihood approach, is often applied to estimation of density for a statitistical distribution. But such an approach produces estimate overfitting on small sets of input data. The estimate overfitting can be eiliminated by such smoothing techniques as regularization or reformulation of constraints

Measurement of income distribution in supranational entities: the case of the European Union

Greater social cohesion is an explicit goal of the European Union. Progress is monitored considering the performance in each member country on the basis of national indicators; EU-wide estimates of inequality and poverty play no role. Yet this is a basic information to evaluate the progress of the Union toward grater social cohesion. This paper examines the methodological requirements of this evaluative exercise, and provides the first estimates of inequality and poverty in the enlarged European Union as if it was a single country.income distribution, inequality, poverty, Euro area, European Union

Composite likelihood for aggregate data from clustered multistate processes under intermittent observation

This is the Accepted Manuscript of this article published by Taylor & Francis in the Communications in Statistics - Theory and Methods on February 11, 2019. The final form of this article is available at https://doi.org/10.1080/03610926.2019.1584310.Markov processes offer a useful basis for modeling the progression of organisms through successive stages of their life cycle. When organisms are examined intermittently in developmental studies, likelihoods can be constructed based on the resulting panel data in terms of transition probability functions. In some settings however, organisms cannot be tracked individually due to a difficulty in identifying distinct individuals, and in such cases aggregate counts of the number of organisms in different stages of development are recorded at successive time points. We consider the setting in which such aggregate counts are available for each of a number of tanks in a developmental study. We develop methods which accommodate clustering of the transition rates within tanks using a marginal modeling approach followed by robust variance estimation, and through use of a random effects model. Composite likelihood is proposed as a basis of inference in both settings. An extension which incorporates mortality is also discussed. The proposed methods are shown to perform well in empirical studies and are applied in an illustrative example on the growth of the Arabidopsis thaliana plant.This research was supported by grants from the Natural Sciences and Engineering Research Council of Canada (RGPIN 155849 and RGPIN 04207) and the Canadian Institutes for Health Research (FRN 13887). Richard Cook is a Tier I Canada Research Chair in Statistical Methods for Health Research