On performance persistence in emerging markets: Empirical evidence from Asia-Pacific

The purpose of this study is to investigate the performance persistence of international mutual funds, employing a data sample which includes 2,168 European mutual funds investing in Asia-Pacific region; Japan excluded. Also, a number of performance measures is tested and compared, and especially, this study tries to find out whether iterative Bayesian procedure can be used to provide more accurate predictions on future performance. Finally, this study examines whether the cross-section of mutual fund returns can be explained with simple accounting variables and market risk. To exclude the effect of the Asian currency crisis in 1997, the studied time period includes years from 1999 to 2007. The overall results showed significant performance persistence for repeating winners when performance was tested with contingency tables. Also the annualized alpha spreads between the top and bottom portfolios were more than ten percent at their highest. Nevertheless, the results do not confirm the improved prediction accuracy of the Bayesian alphas.

Bayesian estimation of diameter distribution during harvesting

Abstract

Effects of polymorphisms in beta1-adrenoceptor and alpha-subunit of G protein on heart rate and blood pressure during exercise test. The finnish cardiovascular study.

J Appl Physiol vol 100, no 2, pp 507-511, 2006

Bayesian classification of forest ecological type from satellite data

The main objective of this study was todo a statistical analysis of ecological type from optical satellite data, using Tipping's sparse Bayesian algorithm. This thesis uses "the Relevence Vector Machine" algorithm in ecological classification betweenforestland and wetland. Further this bi-classification technique was used to do classification of many other different species of trees and produces hierarchical classification of entire subclasses given as a target class. Also, we carried out an attempt to use airborne image of same forest area. Combining it with image analysis, using different image processing operation, we tried to extract good features and later used them to perform classification of forestland and wetland.

Computing the persistent homology of range images with alpha shapes

Laser scanning is becoming an increasingly popular method for measuring 3D objects in industrial design. Laser scanners produce a cloud of 3D points. For CAD software to be able to use such data, however, this point cloud needs to be turned into a vector format. A popular way to do this is to triangulate the assumed surface of the point cloud using alpha shapes. Alpha shapes start from the convex hull of the point cloud and gradually refine it towards the true surface of the object. Often it is nontrivial to decide when to stop this refinement. One criterion for this is to do so when the homology of the object stops changing. This is known as the persistent homology of the object. The goal of this thesis is to develop a way to compute the homology of a given point cloud when processed with alpha shapes, and to infer from it when the persistent homology has been achieved. Practically, the computation of such a characteristic of the target might be applied to power line tower span analysis.

Performance persistence of equity funds in the Russian stock market

This thesis investigates performance persistence among the equity funds investing in Russia during 2003-2007. Fund performance is measured using several methods including the Jensen alpha, the Fama-French 3- factor alpha, the Sharpe ratio and two of its variations. Moreover, we apply the Bayesian shrinkage estimation in performance measurement and evaluate its usefulness compared with the OLS 3-factor alphas. The pattern of performance persistence is analyzed using the Spearman rank correlation test, cross-sectional regression analysis and stacked return time series. Empirical results indicate that the Bayesian shrinkage estimates may provide better and more accurate estimates of fund performance compared with the OLS 3-factor alphas. Secondly, based on the results it seems that the degree of performance persistence is strongly related to length of the observation period. For the full sample period the results show strong signs of performance reversal whereas for the subperiod analysis the results indicate performance persistence during the most recent years.

Bayesian Estimation of Noise

In mathematical modeling the estimation of the model parameters is one of the most common problems. The goal is to seek parameters that fit to the measurements as well as possible. There is always error in the measurements which implies uncertainty to the model estimates. In Bayesian statistics all the unknown quantities are presented as probability distributions. If there is knowledge about parameters beforehand, it can be formulated as a prior distribution. The Bays’ rule combines the prior and the measurements to posterior distribution. Mathematical models are typically nonlinear, to produce statistics for them requires efficient sampling algorithms. In this thesis both Metropolis-Hastings (MH), Adaptive Metropolis (AM) algorithms and Gibbs sampling are introduced. In the thesis different ways to present prior distributions are introduced. The main issue is in the measurement error estimation and how to obtain prior knowledge for variance or covariance. Variance and covariance sampling is combined with the algorithms above. The examples of the hyperprior models are applied to estimation of model parameters and error in an outlier case.

Prognostic Markers and Prolonged Interferon-alpha Therapy in Renal Cell Carcinoma

Molekyylimarkkerit ja pitkäaikainen alfainterferonihoito munuaissyövässä Munuaissyöpäpotilaiden viiden vuoden elossaololuku on noin 50 %. Aikaisempien tutkimuksien mukaan viiden vuoden elossaololuku metastasoituneessa munuaissyövässä on 3-16 %, kun käytettiin alfainterferonia sisältävää hoitoa. Tyypillisesti alfainterferonia on käytetty vähemmäin kuin 6 kuukautta. Avoimia kysymyksiä ovat alfainterferonin optimaalinen hoitoannos ja hoidon kesto yksin tai yhdessä uusien täsmähoitojen kanssa. Tärkeimmät tavoitteet olivat tutkia 1) jaksotetun pitkäaikaisen alfainterferonihoidon tehoa ja siedettävyyttä metastasoituneessa munuaissyövässä ja 2) p53-, Ki-67- ja COX-2-proteiinituotannon ennusteellista merkitystä munuaissyövässä. Tutkimuksessa 117 metastasoituneelle munuaissyöpää sairastaneelle potilaalle etsittiin yksilöllinen hänen sietämänsä maksimaalinen hoitoannos rekombinanttia alfa2a-interferonia (Roferon-ATM). Hoitoa pyrittiin jatkamaan 24 kuukauden ajan. Kolmen hoitoviikon jälkeen pidettiin yhden viikon tauko. Hoito lopetettiin, jos ilmaantui vakavia haittavaikutuksia tai tauti eteni. Toisessa tutkimuksessa proteiinituotanto analysoitiin immunohistokemiallisesti munuaissyöpäpotilaiden kasvainnäytteistä, joita oli säilytetty parafiinissa. Kasvainnäytteet oli otettu talteen munuaisen poistoleikkauksen yhteydessä. Nämä potilaat jaettiin kolmeen eri ryhmään: metastasointi primaarivaiheessa (n=29), metastasointi myöhemmin (n=37) ja ei metastasointia (n=51). Keskimääräinen alfainterferonihoidon kesto oli 11 kuukautta (kk) [0,5 – 32 kk]. Objektiivinen hoitovaste todettiin 17 %:lla, tautitilanne pysyi ennallaan 42 %:lla ja myöhäinen vaste (yli 12 kk:tta hoidon aloittamisesta) todettiin 3 %:lla. Aika vasteen saavuttamisesta taudin etenemiseen oli keskimäärin 8 kk ja elinaika 19,1 kk. Viiden vuoden elossaololuku oli 16 %. Jos metastasoituneella munuaissyöpäpotilaalla oli keuhkometastasointi, hän selvisi todennäköisemmin viisi vuotta kuin muut potilaat. Henkeä uhkaavia sivuvaikutuksia ei todettu. Yli 12 kk:n ajan kestävä alfainterferonihoito on hyödyllistä niille potilaille, jotka ovat saaneet objektiivisen hoitovasteen tai tautitilanne on pysynyt ennallaan. Positiivinen p53- ja Ki-67-ekspressio yhdessä viittaavat suureen metastasoinnin todennäköisyyteen. Positiivinen COX-2-ekspressio viittaa viivästyneeseen metastaasien ilmaantumiseen. Metastasoituneilla potilailla positiiviset p53- ja Ki-67-ekspressiot viittaavat huonoon ennusteeseen, mutta positiivinen COX-2 ekspressio viittaa suotuisaan ennusteeseen. Positiivinen COX-2- ja negatiivinen Ki-67-ekspressio yhdessä viittaavat parantuneeseen ennusteeseen metastasoituneessa munuaissyövässä.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Characterisation of Human Neutral and Lysosomal alpha-Mannosidases

Neutral alpha-mannosidase and lysosomal MAN2B1 alpha-mannosidase belong to glycoside hydrolase family 38, which contains essential enzymes required for the modification and catabolism of asparagine-linked glycans on proteins. MAN2B1 catalyses lysosomal glycan degradation, while neutral α-mannosidase is most likely involved in the catabolism of cytosolic free oligosaccharides. These mannose containing saccharides are generated during glycosylation or released from misfolded glycoproteins, which are detected by quality control in the endoplasmic reticulum. To characterise the biological function of human neutral α-mannosidase, I cloned the alpha-mannosidase cDNA and recombinantly expressed the enzyme. The purified enzyme trimmed the putative natural substrate Man9GlcNAc to Man5GlcNAc, whereas the reducing end GlcNAc2 limited trimming to Man8GlcNAc2. Neutral α-mannosidase showed highest enzyme activity at neutral pH and was activated by the cations Fe₂+, Co2+ and Mn₂+, Cu2+ in turn had a strong inhibitory effect on alpha-mannosidase activity. Analysis of its intracellular localisation revealed that neutral alpha-mannosidase is cytosolic and colocalises with proteasomes. Further work showed that the overexpression of neutral alpha-mannosidase affected the cytosolic free oligosaccharide content and led to enhanced endoplasmic reticulum associated degradation and underglycosylation of secreted proteins. The second part of the study focused on MAN2B1 and the inherited lysosomal storage disorder α-mannosidosis. In this disorder, deficient MAN2B1 activity is associated with mutations in the MAN2B1 gene. The thesis reports the molecular consequences of 35 alpha-mannosidosis associated mutations, including 29 novel missense mutations. According to experimental analyses, the mutations fall into four groups: Mutations, which prevent transport to lysosomes are accompanied with a lack of proteolytic processing of the enzyme (groups 1 and 3). Although the rest of the mutations (groups 2 and 4) allow transport to lysosomes, the mutated proteins are less efficiently processed to their mature form than is wild type MAN2B1. Analysis of the effect of the mutations on the model structure of human lysosomal alpha-mannosidase provides insights on their structural consequences. Mutations, which affect amino acids important for folding (prolines, glycines, cysteines) or domain interface interactions (arginines), arrest the enzyme in the endoplasmic reticulum. Surface mutations and changes, which do not drastically alter residue volume, are tolerated better. Descriptions of the mutations and clinical data are compiled in an α-mannosidosis database, which will be available for the scientific community. This thesis provides a detailed insight into two ubiquitous human alpha-mannosidases. It demonstrates that neutral alpha-mannosidase is involved in the degradation of cytosolic oligosaccharides and suggests that the regulation of this α-mannosidase is important for maintaining the cellular homeostasis of N-glycosylation and glycan degradation. The study on alpha-mannosidosis associated mutations identifies multiple mechanisms for how these mutations are detrimental for MAN2B1 activity. The α-mannosidosis database will benefit both clinicians and scientific research on lysosomal alpha‑mannosidosis.

Communicative and Anticipatory Decision-Making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.