### Browsing by Author "Voutilainen, Marko"

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Item Analysis of a merger in the software industry, case Nordic Solutions and Ravensoft(2014-12-01) Lindén, Fredrik; Voutilainen, Marko; Perustieteiden korkeakoulu; Hämäläinen, MattiMergers and acquisitions have been studied extensively, the reasons for them and how well they perform, but the studies have often focused on large public companies. The goal of this study was to find out if the reasons for mergers are the same for small companies in the software industry, as they are for large companies, and how well these mergers perform. To answer these questions I studied a merger between two small, privately owned Finnish software companies. This study was conducted as a case study, for which I collected data from semi-structured interviews, various presentations, annual reports and surveys. The interviews were done with the management and board members of the companies, which were also the largest shareholders, and all interviews were recorded and transcribed. I found that the reasons for mergers are largely the same for small companies as for larger ones, with the notable difference that small, privately owned companies do not take part in mergers due to management hubris or empire building, as the management is typically the largest shareholders, and as such, do not want to work against the best interest of the shareholders. In this particular case, the merger was ultimately a success, due to the careful selection of the target by the acquirer, although a portion of the merger potential was lost due to unsufficient merger execution planning.Item Modeling and Estimation of Multivariate Discrete and Continuous Time Stationary Processes(Frontiers Research Foundation, 2020-09-17) Voutilainen, Marko; Department of Mathematics and Systems AnalysisIn this paper, we give an autoregressive model of order 1 type of characterization covering all multivariate strictly stationary processes indexed by the set of integers. Consequently, under square integrability, we derive continuous time algebraic Riccati equations for the parameter matrix of the characterization. This provides us with a natural way to define the corresponding estimator. In addition, we show that the estimator inherits consistency from autocovariances of the stationary process. Furthermore, the limiting distribution is given by a linear function of the limiting distribution of the autocovariances. We also present the corresponding existing results of the continuous time setting paralleling them to the discrete case treated in this paper.Item New approaches for modeling and estimation of discrete and continuous time stationary processes(Aalto University, 2020) Voutilainen, Marko; Viitasaari, Lauri, Aalto University, Department of Information and Service Management, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Stochastics and Statistics; Perustieteiden korkeakoulu; School of Science; Ilmonen, Pauliina, Asst. Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandStationary processes form an important class of stochastic processes that has been extensively studied in the literature, and widely applied in many ﬁelds of science. Applications include modeling and forecasting various real-life phenomena such as stock market behavior, sales of a company, natural disasters and velocity of a Brownian particle under the inﬂuence of friction, to mention a few. In this dissertation, we consider new methods for modeling and estimation of discrete and continuous time stationary processes. We characterize discrete and continuous time strictly stationary processes by AR(1) and Langevin equations, respectively. From these characterizations, we derive quadratic (matrix) equations for the corresponding model parameter (matrix) in terms of autocovariance of the stationary process. Based on the equations, we construct an estimator for the model parameter. Furthermore, we show that the estimator inherits consistency and the rate of convergence from the chosen autocovariance estimators. Moreover, its limiting distribution is given by a linear function of the limiting distribution of the autocovariance estimators. In addition, we apply the presented general theory in modeling and estimationof a generalization of the ARCH model with stationary liquidity.Item Note on AR(1)-characterisation of stationary processes and model fitting(VTeX, 2019-06) Voutilainen, Marko; Viitasaari, Lauri; Ilmonen, Pauliina; Department of Mathematics and Systems Analysis; University of Helsinki; Statistics and Mathematical Data ScienceIt was recently proved that any strictly stationary stochastic process can be viewed as an autoregressive process of order one with coloured noise. Furthermore, it was proved that, using this characterisation, one can define closed form estimators for the model parameter based on autocovariance estimators for several different lags. However, this estimation procedure may fail in some special cases. In this article, a detailed analysis of these special cases is provided. In particular, it is proved that these cases correspond to degenerate processes.Item Note on Asymptotic Behavior of Spatial Sign Autocovariance Matrices(Elsevier, 2023-01) Voutilainen, Marko; Ilmonen, Pauliina; Viitasaari, Lauri; Lietzen, Niko; University of Turku; Statistics and Mathematical Data Science; Uppsala University; Department of Mathematics and Systems AnalysisIn this paper, we consider the asymptotic properties of the spatial sign autocovariance matrix for Gaussian subordinated processes with a known location parameter.Item On model fitting and estimation of strictly stationary processes(VTeX, 2017) Voutilainen, Marko; Viitasaari, Lauri; Ilmonen, Pauliina; Department of Mathematics and Systems Analysis; University of Helsinki; Statistics and Mathematical Data ScienceStationary processes have been extensively studied in the literature. Their applications include modeling and forecasting numerous real life phenomena such as natural disasters, sales and market movements. When stationary processes are considered, modeling is traditionally based on fitting an autoregressive moving average (ARMA) process. However, we challenge this conventional approach. Instead of fitting an ARMA model, we apply an AR(1) characterization in modeling any strictly stationary processes. Moreover, we derive consistent and asymptotically normal estimators of the corresponding model parameter.Item On the ARCH model with stationary liquidity(Springer Verlag, 2021-02) Voutilainen, Marko; Ilmonen, Pauliina; Torres, Soledad; Tudor, Ciprian; Viitasaari, Lauri; Department of Mathematics and Systems Analysis; Statistics and Mathematical Data Science; Universidad de Valparaíso; University of Lille; Department of Information and Service ManagementThe classical ARCH model together with its extensions have been widely applied in the modeling of financial time series. We study a variant of the ARCH model that takes account of liquidity given by a positive stationary process. We provide minimal assumptions that ensure the existence and uniqueness of the stationary solution for this model. Moreover, we give necessary and sufficient conditions for the existence of the autocovariance function. After that, we derive an AR(1) characterization for the stationary solution yielding Yule–Walker type quadratic equations for the model parameters. In order to define a proper estimation method for the model, we first show that the autocovariance estimators of the stationary solution are consistent under relatively mild assumptions. Consequently, we prove that the natural estimators arising out of the quadratic equations inherit consistency from the autocovariance estimators. Finally, we illustrate our results with several examples and a simulation study.Item Should Employee Incentives Be Constructed As Bonuses Or Penalties And Are Team Incentives Better?(2021) Voutilainen, Marko; Murto, Pauli; Hafner, Flavio; Taloustieteen laitos; Kauppakorkeakoulu; School of BusinessItem Sobolevin avaruudet(2013-09-24) Voutilainen, Marko; Kinnunen, Juha; Perustieteiden korkeakoulu; Kinnunen, JuhaItem Triangulations of the topological closed disk and circle packings(2016-06-14) Voutilainen, Marko; Ivarsson, Björn; Kytölä, Kalle; Perustieteiden korkeakoulu; Kytölä, KalleThe main studies of this thesis are triangulations of the topological closed disk and circle packings as providers of embeddings in the hyperbolic disk for such triangulations. Triangulations are first introduced for a more general class of topological surfaces, before focusing on triangulations of the closed disk. The combinatorial nature of triangulations is revealed and it is used to identify triangulations. Construction of bijections between sets of triangulations leads to a recursive formula for the number of rooted triangulations with given number of boundary and interior vertices. After writing the recursion in terms of generating functions, an explicit formula for the number of rooted triangulations is derived. The methods used to derive the recursive formula are also used to uniform sampling of rooted triangulations. Circle packings are introduced at first in more general context, before concentrating on circle packings in the hyperbolic disk. The main result is that for every triangulation of the topological closed disk, there exists the maximal circle packing in the hyperbolic disk obeying the combinatorics of the triangulation. This maximal circle packing provides us with an embedding of the triangulation in the disk. These embeddings are used to visualize a collection of uniform random rooted triangulations. In the final chapter, a definition for uniform probability measures on classes of rooted triangulations with fixed number of vertices is provided. After that, random boundary length variables from the classes to natural numbers is defined and proved that the random boundary length converges in distribution to a non-degenerate random variable, as the number of vertices tends to infinity. Respectively, after defining probability measures on classes of rooted triangulations with fixed boundary length, it is shown that an appropriately renormalized random number of vertices converges in distribution to a non-degenerate random variable, as the boundary length tends to infinity.Item Using desktop-videoconferencing in distance education(1998) Voutilainen, Marko; Kouti, Sakari; Teknillisen fysiikan ja matematiikan osasto; Teknillinen korkeakoulu; Helsinki University of Technology; Ehtamo, HarriTämä diplomityö on kirjallisuustutkielma videoneuvottelujärjestelmistä ja niiden soveltamisesta opetukseen. Tavoitteena on antaa lukijalle hyvä yleiskuva ko. järjestelmistä ja niillä käytävistä neuvotteluista sekä niiden tuomista hyödyistä yrityksille. Työssä esitellään tärkeimmät videoneuvottelujärjestelmiä koskevat standardit ja niitä hyödyntävät sovellukset. Diplomityössä selitetään videoneuvottelun hyödyntämismahdollisuuksia etäopetukseen ja sen asettamia erityisvaatimuksia videoneuvottelulaitteille. Lisäksi tarkastellaan videoneuvottelun asettamia erityisvaatimuksia esitettävälle opetusmateriaalille. Kirjallisuustutkielman pääpaino on ISDN:ää käyttävissä videoneuvottelujärjestelmissä. Lisäksi tehdään kokeellisia testejä käyttäen lähiverkossa toimivia videoneuvottelulaitteistoja. Diplomityössä tutkitaan ainoastaan PC-mikroihin liitettäviä videoneuvottelujärjestelmiä. Työssä ei tutkita eikä arvioida studiotasoisia videoneuvottelujärjestelmiä.Item Vector-valued generalized Ornstein–Uhlenbeck processes: Properties and parameter estimation(WILEY-BLACKWELL, 2022-09) Voutilainen, Marko; Viitasaari, Lauri; Ilmonen, Pauliina; Torres, Soledad; Tudor, Ciprian; Department of Mathematics and Systems Analysis; Department of Information and Service Management; Statistics and Mathematical Data ScienceGeneralizations of the Ornstein-Uhlenbeck process defined through Langevin equations, such as fractional Ornstein-Uhlenbeck processes, have recently received a lot of attention. However, most of the literature focuses on the one-dimensional case with Gaussian noise. In particular, estimation of the unknown parameter is widely studied under Gaussian stationary increment noise. In this article, we consider estimation of the unknown model parameter in the multidimensional version of the Langevin equation, where the parameter is a matrix and the noise is a general, not necessarily Gaussian, vector-valued process with stationary increments. Based on algebraic Riccati equations, we construct an estimator for the parameter matrix. Moreover, we prove the consistency of the estimator and derive its limiting distribution under natural assumptions. In addition, to motivate our work, we prove that the Langevin equation characterizes essentially all multidimensional stationary processes.