Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

The effects of monetary policy on stock market bubbles: Some evidence

We estimate the response of stock prices to exogenous monetary policy shocks usinga vector-autoregressive model with time-varying parameters. Our evidence points toprotracted episodes in which, after a a short-run decline, stock prices increase persistently in response to an exogenous tightening of monetary policy. That responseis clearly at odds with the "conventional" view on the effects of monetary policy onbubbles, as well as with the predictions of bubbleless models. We also argue that it isunlikely that such evidence be accounted for by an endogenous response of the equitypremium to the monetary policy shocks.

Partial correlation : Network analysis

The final year project came to us as an opportunity to get involved in a topic which has appeared to be attractive during the learning process of majoring in economics: statistics and its application to the analysis of economic data, i.e. econometrics.Moreover, the combination of econometrics and computer science is a very hot topic nowadays, given the Information Technologies boom in the last decades and the consequent exponential increase in the amount of data collected and stored day by day. Data analysts able to deal with Big Data and to find useful results from it are verydemanded in these days and, according to our understanding, the work they do, although sometimes controversial in terms of ethics, is a clear source of value added both for private corporations and the public sector. For these reasons, the essence of this project is the study of a statistical instrument valid for the analysis of large datasets which is directly related to computer science: Partial Correlation Networks.The structure of the project has been determined by our objectives through the development of it. At first, the characteristics of the studied instrument are explained, from the basic ideas up to the features of the model behind it, with the final goal of presenting SPACE model as a tool for estimating interconnections in between elements in large data sets. Afterwards, an illustrated simulation is performed in order to show the power and efficiency of the model presented. And at last, the model is put into practice by analyzing a relatively large data set of real world data, with the objective of assessing whether the proposed statistical instrument is valid and useful when applied to a real multivariate time series. In short, our main goals are to present the model and evaluate if Partial Correlation Network Analysis is an effective, useful instrument and allows finding valuable results from Big Data.As a result, the findings all along this project suggest the Partial Correlation Estimation by Joint Sparse Regression Models approach presented by Peng et al. (2009) to work well under the assumption of sparsity of data. Moreover, partial correlation networks are shown to be a very valid tool to represent cross-sectional interconnections in between elements in large data sets.The scope of this project is however limited, as there are some sections in which deeper analysis would have been appropriate. Considering intertemporal connections in between elements, the choice of the tuning parameter lambda, or a deeper analysis of the results in the real data application are examples of aspects in which this project could be completed.To sum up, the analyzed statistical tool has been proved to be a very useful instrument to find relationships that connect the elements present in a large data set. And after all, partial correlation networks allow the owner of this set to observe and analyze the existing linkages that could have been omitted otherwise.

Gold investments and short- and long-run price determinants of the price of gold

Tämän tutkielman tavoitteena on tutkia tekijöitä jotkavaikuttavat lyhyellä ja pitkällä aikavälillä kullan hintaan. Toiseksi tutkielmassa selvitetään mitä eri sijoitusmahdollisuuksia löytyy kultaan sijoitettaessa. Aineistona käytetään kuukausitasoista dataa Yhdysvaltain ja maailman hintaindekseistä, Yhdysvaltain ja maailman inflaatiosta ja inflaation volatiliteetista, kullan beetasta, kullan lainahinnasta, luottoriskistä ja Yhdysvaltojen ja maailman valuuttakurssi indeksistä joulukuulta 1972 elokuulle 2006. Yhteisintegraatio regressiotekniikoita käytettiin muodostamaan malli jonka avullatutkittiin päätekijöitä jotka vaikuttavat kullan hintaan. Kirjallisuutta tutkimalla selvitettiin miten kultaan voidaan sijoittaa. Empiirisettulokset ovat yhteneväisiä edellisten tutkimusten kanssa. Tukea löytyi sille, että kulta on pitkän ajan suoja inflaatiota vastaan ja kulta ja Yhdysvaltojen inflaatio liikkuvat pitkällä aikavälillä yhdessä. Kullan hintaan vaikuttavat kuitenkin lyhyen ajan tekijät pitkän ajan tekijöitä enemmän. Kulta on myös sijoittajalle helppo sijoituskohde, koska se on hyvin saatavilla markkinoilla ja eri instrumentteja on lukuisia.

The JAK/STAT3 Pathway Is a Common Inducer of Astrocyte Reactivity in Alzheimer's and Huntington's Diseases.

Astrocyte reactivity is a hallmark of neurodegenerative diseases (ND), but its effects on disease outcomes remain highly debated. Elucidation of the signaling cascades inducing reactivity in astrocytes during ND would help characterize the function of these cells and identify novel molecular targets to modulate disease progression. The Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3) pathway is associated with reactive astrocytes in models of acute injury, but it is unknown whether this pathway is directly responsible for astrocyte reactivity in progressive pathological conditions such as ND. In this study, we examined whether the JAK/STAT3 pathway promotes astrocyte reactivity in several animal models of ND. The JAK/STAT3 pathway was activated in reactive astrocytes in two transgenic mouse models of Alzheimer's disease and in a mouse and a nonhuman primate lentiviral vector-based model of Huntington's disease (HD). To determine whether this cascade was instrumental for astrocyte reactivity, we used a lentiviral vector that specifically targets astrocytes in vivo to overexpress the endogenous inhibitor of the JAK/STAT3 pathway [suppressor of cytokine signaling 3 (SOCS3)]. SOCS3 significantly inhibited this pathway in astrocytes, prevented astrocyte reactivity, and decreased microglial activation in models of both diseases. Inhibition of the JAK/STAT3 pathway within reactive astrocytes also increased the number of huntingtin aggregates, a neuropathological hallmark of HD, but did not influence neuronal death. Our data demonstrate that the JAK/STAT3 pathway is a common mediator of astrocyte reactivity that is highly conserved between disease states, species, and brain regions. This universal signaling cascade represents a potent target to study the role of reactive astrocytes in ND.

Stock and Bond Market Integration: Evidence from Russian Financial Markets

Minimizing the risks of an investment portfolio but not in the favour of expected returns is one of the key interests of an investor. Typically, portfolio diversification is achieved using two main strategies: investing in different classes of assets thought to have little or negative correlations or investing in similar classes of assets in multiple markets through international diversification. This study investigates integration of the Russian financial markets in the time period of January 1, 2003 to December 28, 2007 using daily data. The aim is to test the intra-country and cross-country integration of the Russian stock and bond markets between seven countries. Our test methodology for the short-run dynamics testing is the vector autoregressive model (VAR) and for the long-run cointegration testing we use the Johansen cointegration test which is an extension to VAR. The empirical results of this study show that the Russian stock and bond markets are not integrated in the long-run either at intra-country or cross-country level which means that the markets are relatively segmented. The short-run dynamics are also relatively low. This implies a presence of potential gains from diversification.

A dynamic analysis of asymmetric shocks in EU manufacturing

Available empirical evidence regarding the degree of symmetry between European economies in the context of Monetary Unification is not conclusive. This paper offers new empirical evidence concerning this issue related to the manufacturing sector. Instead of using a static approach as most empirical studies do, we analyse the dynamic evolution of shock symmetry using a state-space model. The results show a clear reduction of asymmetries in terms of demand shocks between 1975 and 1996, with an increase in terms of supply shocks at the end of the period.

Kaksisuuntaisen DC/DC-hakkurin virtasäätö polttokennosovelluksessa

Ilmastonmuutos ja fossiilisten polttoaineiden ehtyminen ovat edesauttaneet uusiutuvien energialähteiden tutkimusta huomattavasti. Lisäksi alati kasvava sähköenergian tarve lisää hajautetun sähköntuotannon ja vaihtoehtoisten energialähteiden kiinnostavuutta. Yleisimpiä hajautetun sähköntuotannon energialähteitä ovat tuulivoima, aurinkovoima ja uutena tulokkaana polttokennot. Polttokennon kytkeminen sähköverkkoon vaatii tehoelektroniikkaa, ja yleensä yksinkertaisessa polttokennosovelluksessa polttokenno kytketään galvaanisesti erottavan yksisuuntaisen DC/DC-hakkurin ja vaihtosuuntaajan kanssa sarjaan. Polttokennon rinnalla voidaan käyttää akkua tasaamaan polttokennon syöttämää jännitettä, jolloin akun ja polttokennon väliin tarvitaan kaksisuuntainen DC/DC-hakkuri, joka pystyy siirtämään energiaa molempiin suuntiin. Tässä diplomityössä on esitetty kaksisuuntaisen DC/DC-hakkurin tilayhtälökeskiarvoistusmenetelmään perustuva malli sekä mallin perusteella toteutettu virtasäätö. Tutkittava hakkuritopologia on kokosilta-tyyppinen boost-hakkuri, ja säätömenetelmä keskiarvovirtasäätö. Työn tuloksena syntyi tilayhtälömalli kaksisuuntaiselle FB boost -hakkurille sekä sen tulokelan virran säätämiseen soveltuva säädin. Säädin toimii normaalitilanteissa hyvin, mutta erikoistilanteissa, kuten hakkurin tulojännitteen äkillisessä muutostilanteessa, vaadittaisiin tehokkaampi säädin, jolla saavutettaisiin nopeampi nousuaika ilman ylitystä ja oskillointia.

Ionsonde Measurement Trend

Time series analysis has gone through different developmental stages before the current modern approaches. These can broadly categorized as the classical time series analysis and modern time series analysis approach. In the classical one, the basic target of the analysis is to describe the major behaviour of the series without necessarily dealing with the underlying structures. On the contrary, the modern approaches strives to summarize the behaviour of the series going through its underlying structure so that the series can be represented explicitly. In other words, such approach of time series analysis tries to study the series structurally. The components of the series that make up the observation such as the trend, seasonality, regression and disturbance terms are modelled explicitly before putting everything together in to a single state space model which give the natural interpretation of the series. The target of this diploma work is to practically apply the modern approach of time series analysis known as the state space approach, more specifically, the dynamic linear model, to make trend analysis over Ionosonde measurement data. The data is time series of the peak height of F2 layer symbolized by hmF2 which is the height of high electron density. In addition, the work also targets to investigate the connection between solar activity and the peak height of F2 layer. Based on the result found, the peak height of the F2 layer has shown a decrease during the observation period and also shows a nonlinear positive correlation with solar activity.

Cohomologie de fibrés en droite sur le fibré cotangent de variétés grassmanniennes généralisées

Cette thèse s'intéresse à la cohomologie de fibrés en droite sur le fibré cotangent de variétés projectives. Plus précisément, pour $G$ un groupe algébrique simple, connexe et simplement connexe, $P$ un sous-groupe maximal de $G$ et $\omega$ un générateur dominant du groupe de caractères de $P$, on cherche à comprendre les groupes de cohomologie $H^i(T^*(G/P),\mathcal{L})$ où $\mathcal{L}$ est le faisceau des sections d'un fibré en droite sur $T^*(G/P)$. Sous certaines conditions, nous allons montrer qu'il existe un isomorphisme, à graduation près, entre $H^i(T^*(G/P),\mathcal{L})$ et $H^i(T^*(G/P),\mathcal{L}^{\vee})$ Après avoir travaillé dans un contexte théorique, nous nous intéresserons à certains sous-groupes paraboliques en lien avec les orbites nilpotentes. Dans ce cas, l'algèbre de Lie du radical unipotent de $P$, que nous noterons $\nLie$, a une structure d'espace vectoriel préhomogène. Nous pourrons alors déterminer quels cas vérifient les hypothèses nécessaires à la preuve de l'isomorphisme en montrant l'existence d'un $P$-covariant $f$ dans $\comp[\nLie]$ et en étudiant ses propriétés. Nous nous intéresserons ensuite aux singularités de la variété affine $V(f)$. Nous serons en mesure de montrer que sa normalisation est à singularités rationnelles.

Analysis discrete function theory

There is a recent trend to describe physical phenomena without the use of infinitesimals or infinites. This has been accomplished replacing differential calculus by the finite difference theory. Discrete function theory was first introduced in l94l. This theory is concerned with a study of functions defined on a discrete set of points in the complex plane. The theory was extensively developed for functions defined on a Gaussian lattice. In 1972 a very suitable lattice H: {Ci qmxO,I qnyo), X0) 0, X3) 0, O < q < l, m, n 5 Z} was found and discrete analytic function theory was developed. Very recently some work has been done in discrete monodiffric function theory for functions defined on H. The theory of pseudoanalytic functions is a generalisation of the theory of analytic functions. When the generator becomes the identity, ie., (l, i) the theory of pseudoanalytic functions reduces to the theory of analytic functions. Theugh the theory of pseudoanalytic functions plays an important role in analysis, no discrete theory is available in literature. This thesis is an attempt in that direction. A discrete pseudoanalytic theory is derived for functions defined on H.

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Central notations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform. In this way very elaborated aspects of mathematical statistics can be understood easily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating, combination of likelihood and robust M-estimation functions are simple additions/ perturbations in A2(Pprior). Weighting observations corresponds to a weighted addition of the corresponding evidence. Likelihood based statistics for general exponential families turns out to have a particularly easy interpretation in terms of A2(P). Regular exponential families form finite dimensional linear subspaces of A2(P) and they correspond to finite dimensional subspaces formed by their posterior in the dual information space A2(Pprior). The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P. The discussion of A2(P) valued random variables, such as estimation functions or likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning

Application of constrained predictive control on a 3D crane system

This paper describes the SIMULINK implementation of a constrained predictive control algorithm based on quadratic programming and linear state space models, and its application to a laboratory-scale 3D crane system. The algorithm is compatible with Real Time. Windows Target and, in the case of the crane system, it can be executed with a sampling period of 0.01 s and a prediction horizon of up to 300 samples, using a linear state space model with 3 inputs, 5 outputs and 13 states.

Subspace system identification framework for the analysis of multimoded propagation of THz-transient signals

We provide a system identification framework for the analysis of THz-transient data. The subspace identification algorithm for both deterministic and stochastic systems is used to model the time-domain responses of structures under broadband excitation. Structures with additional time delays can be modelled within the state-space framework using additional state variables. We compare the numerical stability of the commonly used least-squares ARX models to that of the subspace N4SID algorithm by using examples of fourth-order and eighth-order systems under pulse and chirp excitation conditions. These models correspond to structures having two and four modes simultaneously propagating respectively. We show that chirp excitation combined with the subspace identification algorithm can provide a better identification of the underlying mode dynamics than the ARX model does as the complexity of the system increases. The use of an identified state-space model for mode demixing, upon transformation to a decoupled realization form is illustrated. Applications of state-space models and the N4SID algorithm to THz transient spectroscopy as well as to optical systems are highlighted.

Comparison of subspace and ARX models of a waveguide's terahertz transient response after optimal wavelet filtering

A quasi-optical deembedding technique for characterizing waveguides is demonstrated using wide-band time-resolved terahertz spectroscopy. A transfer function representation is adopted for the description of the signal in the input and output port of the waveguides. The time-domain responses were discretized and the waveguide transfer function was obtained through a parametric approach in the z-domain after describing the system with an AutoRegressive with eXogenous input (ARX), as well as with a state-space model. Prior to the identification procedure, filtering was performed in the wavelet domain to minimize both signal distortion, as well as the noise propagating in the ARX and subspace models. The optimal filtering procedure used in the wavelet domain for the recorded time-domain signatures is described in detail. The effect of filtering prior to the identification procedures is elucidated with the aid of pole-zero diagrams. Models derived from measurements of terahertz transients in a precision WR-8 waveguide adjustable short are presented.