Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach

A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source–sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.

On the estimation and comparison of short-rate models using the generalised method of moments

Subsequent to the influential paper of [Chan, K.C., Karolyi, G.A., Longstaff, F.A., Sanders, A.B., 1992. An empirical comparison of alternative models of the short-term interest rate. Journal of Finance 47, 1209-1227], the generalised method of moments (GMM) has been a popular technique for estimation and inference relating to continuous-time models of the short-term interest rate. GMM has been widely employed to estimate model parameters and to assess the goodness-of-fit of competing short-rate specifications. The current paper conducts a series of simulation experiments to document the bias and precision of GMM estimates of short-rate parameters, as well as the size and power of [Hansen, L.P., 1982. Large sample properties of generalised method of moments estimators. Econometrica 50, 1029-1054], J-test of over-identifying restrictions. While the J-test appears to have appropriate size and good power in sample sizes commonly encountered in the short-rate literature, GMM estimates of the speed of mean reversion are shown to be severely biased. Consequently, it is dangerous to draw strong conclusions about the strength of mean reversion using GMM. In contrast, the parameter capturing the levels effect, which is important in differentiating between competing short-rate specifications, is estimated with little bias. (c) 2006 Elsevier B.V. All rights reserved.

Modelling of non-stationary processes using radial basis function networks

This paper reports preliminary progress on a principled approach to modelling nonstationary phenomena using neural networks. We are concerned with both parameter and model order complexity estimation. The basic methodology assumes a Bayesian foundation. However to allow the construction of pragmatic models, successive approximations have to be made to permit computational tractibility. The lowest order corresponds to the (Extended) Kalman filter approach to parameter estimation which has already been applied to neural networks. We illustrate some of the deficiencies of the existing approaches and discuss our preliminary generalisations, by considering the application to nonstationary time series.

An improved novelty criterion for resource allocating networks

Online model order complexity estimation remains one of the key problems in neural network research. The problem is further exacerbated in situations where the underlying system generator is non-stationary. In this paper, we introduce a novelty criterion for resource allocating networks (RANs) which is capable of being applied to both stationary and slowly varying non-stationary problems. The deficiencies of existing novelty criteria are discussed and the relative performances are demonstrated on two real-world problems : electricity load forecasting and exchange rate prediction.

The difference model with guessing explains interval bias in two-alternative forced- chocice detection procedures

The standard difference model of two-alternative forced-choice (2AFC) tasks implies that performance should be the same when the target is presented in the first or the second interval. Empirical data often show “interval bias” in that percentage correct differs significantly when the signal is presented in the first or the second interval. We present an extension of the standard difference model that accounts for interval bias by incorporating an indifference zone around the null value of the decision variable. Analytical predictions are derived which reveal how interval bias may occur when data generated by the guessing model are analyzed as prescribed by the standard difference model. Parameter estimation methods and goodness-of-fit testing approaches for the guessing model are also developed and presented. A simulation study is included whose results show that the parameters of the guessing model can be estimated accurately. Finally, the guessing model is tested empirically in a 2AFC detection procedure in which guesses were explicitly recorded. The results support the guessing model and indicate that interval bias is not observed when guesses are separated out.

A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses

Estimates of HIV prevalence are important for policy in order to establish the health status of a country's population and to evaluate the effectiveness of population-based interventions and campaigns. However, participation rates in testing for surveillance conducted as part of household surveys, on which many of these estimates are based, can be low. HIV positive individuals may be less likely to participate because they fear disclosure, in which case estimates obtained using conventional approaches to deal with missing data, such as imputation-based methods, will be biased. We develop a Heckman-type simultaneous equation approach which accounts for non-ignorable selection, but unlike previous implementations, allows for spatial dependence and does not impose a homogeneous selection process on all respondents. In addition, our framework addresses the issue of separation, where for instance some factors are severely unbalanced and highly predictive of the response, which would ordinarily prevent model convergence. Estimation is carried out within a penalized likelihood framework where smoothing is achieved using a parametrization of the smoothing criterion which makes estimation more stable and efficient. We provide the software for straightforward implementation of the proposed approach, and apply our methodology to estimating national and sub-national HIV prevalence in Swaziland, Zimbabwe and Zambia.

Estimation of leaf area in grapevine cv. Syrah using empirical models

Aiming to obtain empirical models for the estimation of Syrah leaf area a set of 210 fruiting shoots was randomly collected during the 2013 growing season in an adult experimental vineyard, located in Lisbon, Portugal. Samples of 30 fruiting shoots were taken periodically from the stage of inflorescences visible to veraison (7 sampling dates). At the lab, from each shoot, primary and lateral leaves were separated and numbered according to node insertion. For each leaf, the length of the central and lateral veins was recorded and then the leaf area was measured by a leaf area meter. For single leaf area estimation the best statistical models uses as explanatory variable the sum of the lengths of the two lateral leaf veins. For the estimation of leaf area per shoot it was followed the approach of Lopes & Pinto (2005), based on 3 explanatory variables: number of primary leaves and area of the largest and smallest leaves. The best statistical model for estimation of primary leaf area per shoot uses a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves. For lateral leaf area estimation another model using the same type of calculated variable is also presented. All models explain a very high proportion of variability in leaf area. Our results confirm the already reported strong importance of the three measured variables (number of leaves and area of the largest and smallest leaf) as predictors of the shoot leaf area. The proposed models can be used to accurately predict Syrah primary and secondary leaf area per shoot in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialized staff or expensive equipment.

Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to and the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum. (C) 1997 Optical Society of America.

The condensation and ordering of models of empty liquids

We consider a simple model consisting of particles with four bonding sites ("patches"), two of type A and two of type B, on the square lattice, and investigate its global phase behavior by simulations and theory. We set the interaction between B patches to zero and calculate the phase diagram as the ratio between the AB and the AA interactions, epsilon(AB)*, varies. In line with previous work, on three-dimensional off-lattice models, we show that the liquid-vapor phase diagram exhibits a re-entrant or "pinched" shape for the same range of epsilon(AB)*, suggesting that the ratio of the energy scales - and the corresponding empty fluid regime - is independent of the dimensionality of the system and of the lattice structure. In addition, the model exhibits an order-disorder transition that is ferromagnetic in the re-entrant regime. The use of low-dimensional lattice models allows the simulation of sufficiently large systems to establish the nature of the liquid-vapor critical points and to describe the structure of the liquid phase in the empty fluid regime, where the size of the "voids" increases as the temperature decreases. We have found that the liquid-vapor critical point is in the 2D Ising universality class, with a scaling region that decreases rapidly as the temperature decreases. The results of simulations and theoretical analysis suggest that the line of order-disorder transitions intersects the condensation line at a multi-critical point at zero temperature and density, for patchy particle models with a re-entrant, empty fluid, regime. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3657406]

Extreme value Birnbaum-Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

Evolutionary history of almond tree domestication in the Mediterranean basin.

Genetic diversity of contemporary domesticated species is shaped by both natural and human-driven processes. However, until now, little is known about how domestication has imprinted the variation of fruit tree species. In this study, we reconstruct the recent evolutionary history of the domesticated almond tree, Prunus dulcis, around the Mediterranean basin, using a combination of nuclear and chloroplast microsatellites [i.e. simple sequence repeat (SSRs)] to investigate patterns of genetic diversity. Whereas conservative chloroplast SSRs show a widespread haplotype and rare locally distributed variants, nuclear SSRs show a pattern of isolation by distance with clines of diversity from the East to the West of the Mediterranean basin, while Bayesian genetic clustering reveals a substantial longitudinal genetic structure. Both kinds of markers thus support a single domestication event, in the eastern side of the Mediterranean basin. In addition, model-based estimation of the timing of genetic divergence among those clusters is estimated sometime during the Holocene, a result that is compatible with human-mediated dispersal of almond tree out of its centre of origin. Still, the detection of region-specific alleles suggests that gene flow from relictual wild preglacial populations (in North Africa) or from wild counterparts (in the Near East) could account for a fraction of the diversity observed.

Monitoring of centrifugal pump operation by a frequency converter

Centrifugal pumps are widely used in industrial and municipal applications, and they are an important end-use application of electric energy. However, in many cases centrifugal pumps operate with a significantly lower energy efficiency than they actually could, which typically has an increasing effect on the pump energy consumption and the resulting energy costs. Typical reasons for this are the incorrect dimensioning of the pumping system components and inefficiency of the applied pump control method. Besides the increase in energy costs, an inefficient operation may increase the risk of a pump failure and thereby the maintenance costs. In the worst case, a pump failure may lead to a process shutdown accruing additional costs. Nowadays, centrifugal pumps are often controlled by adjusting their rotational speed, which affects the resulting flow rate and output pressure of the pumped fluid. Typically, the speed control is realised with a frequency converter that allows the control of the rotational speed of an induction motor. Since a frequency converter can estimate the motor rotational speed and shaft torque without external measurement sensors on the motor shaft, it also allows the development and use of sensorless methods for the estimation of the pump operation. Still today, the monitoring of pump operation is based on additional measurements and visual check-ups, which may not be applicable to determine the energy efficiency of the pump operation. This doctoral thesis concentrates on the methods that allow the use of a frequency converter as a monitoring and analysis device for a centrifugal pump. Firstly, the determination of energy-efficiency- and reliability-based limits for the recommendable operating region of a variable-speed-driven centrifugal pump is discussed with a case study for the laboratory pumping system. Then, three model-based estimation methods for the pump operating location are studied, and their accuracy is determined by laboratory tests. In addition, a novel method to detect the occurrence of cavitation or flow recirculation in a centrifugal pump by a frequency converter is introduced. Its sensitivity compared with known cavitation detection methods is evaluated, and its applicability is verified by laboratory measurements for three different pumps and by using two different frequency converters. The main focus of this thesis is on the radial flow end-suction centrifugal pumps, but the studied methods can also be feasible with mixed and axial flow centrifugal pumps, if allowed by their characteristics.

Tavoitevarastoarvojen määrittäminen uusille tuotteille - case: päivittäistavarakaupan yritys

Tämän tutkielman tarkoituksena oli löytää keinoja uusien tuotteiden tavoitevarastoarvojen määrittämisen helpottamiseksi case-yrityksessä. Ongelma on seurausta siitä, ettei uusille tuotteille ole saatavissa kysyntädataa. Oikeelliset tavoitevarastoarvot on tärkeitä case-yritykselle hävikin minimoimisen ja erityisesti asiakastyytyväisyyden vuoksi. Tutkittavaa aihetta lähestyttiin päivittäistavarakaupan strategisten menestystekijöiden sekä varastonhallinnan kautta. Teoriaosuuden pohjalta toteutettiin empiirinen osio, jossa etsittiin case-yrityksen kahden tuoteryhmän kysyntää selittäviä tekijöitä paneeliregression avulla. Ensin kuitenkin tutkittiin tuoteryhmien yleistä kysynnän rakennetta erilaisten kysyntäkuvaajien sekä korrelaatiomatriisien avulla. Tavoitevarastoarvoon eniten vaikuttava tekijä on kysyntä. Brändi ja hinta muodostuivat puolestaan vahvimmiksi kysyntää määrittäviksi muuttujiksi. Kysyntää arvioitaessa muut sitä nostavat muuttujat tulee aina miettiä tapauskohtaisesti. Mehukeitoilla kysyntää nostivat eniten vahva brändi ja suuri pakkauskoko. Juustoraasteilla puolestaan alhainen hinta ja pieni pakkauskoko olivat keskeisimmät myyntiin vaikuttavat tekijät. Tuoteryhmien muut kysyntää nostavat ominaisuudet olivat samoja, kuin tuoteryhmän suosituimmilla tuotteilla.

Reproducibility of heart rate variability parameters measured in healthy subjects at rest and after a postural change maneuver

Heart rate variability (HRV) provides important information about cardiac autonomic modulation. Since it is a noninvasive and inexpensive method, HRV has been used to evaluate several parameters of cardiovascular health. However, the internal reproducibility of this method has been challenged in some studies. Our aim was to determine the intra-individual reproducibility of HRV parameters in short-term recordings obtained in supine and orthostatic positions. Electrocardiographic (ECG) recordings were obtained from 30 healthy subjects (20-49 years, 14 men) using a digital apparatus (sampling ratio = 250 Hz). ECG was recorded for 10 min in the supine position and for 10 min in the orthostatic position. The procedure was repeated 2-3 h later. Time and frequency domain analyses were performed. Frequency domain included low (LF, 0.04-0.15 Hz) and high frequency (HF, 0.15-0.4 Hz) bands. Power spectral analysis was performed by the autoregressive method and model order was set at 16. Intra-subject agreement was assessed by linear regression analysis, test of difference in variances and limits of agreement. Most HRV measures (pNN50, RMSSD, LF, HF, and LF/HF ratio) were reproducible independent of body position. Better correlation indexes (r > 0.6) were obtained in the orthostatic position. Bland-Altman plots revealed that most values were inside the agreement limits, indicating concordance between measures. Only SDNN and NNv in the supine position were not reproducible. Our results showed reproducibility of HRV parameters when recorded in the same individual with a short time between two exams. The increased sympathetic activity occurring in the orthostatic position probably facilitates reproducibility of the HRV indexes.