Search results for "estimointi"
showing 10 items of 33 documents
Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance
2017
We establish an ordering criterion for the asymptotic variances of two consistent Markov chain Monte Carlo (MCMC) estimators: an importance sampling (IS) estimator, based on an approximate reversible chain and subsequent IS weighting, and a standard MCMC estimator, based on an exact reversible chain. Essentially, we relax the criterion of the Peskun type covariance ordering by considering two different invariant probabilities, and obtain, in place of a strict ordering of asymptotic variances, a bound of the asymptotic variance of IS by that of the direct MCMC. Simple examples show that IS can have arbitrarily better or worse asymptotic variance than Metropolis-Hastings and delayed-acceptanc…
Estimating Mean Lifetime from Partially Observed Events in Nuclear Physics
2022
Abstract The mean lifetime is an important characteristic of particles to be identified in nuclear physics. State-of-the-art particle detectors can identify the arrivals of single radioactive nuclei as well as their subsequent radioactive decays (departures). Challenges arise when the arrivals and departures are unmatched and the departures are only partially observed. An inefficient solution is to run experiments where the arrival rate is set very low to allow for the matching of arrivals and departures. We propose an estimation method that works for a wide range of arrival rates. The method combines an initial estimator and a numerical bias correction technique. Simulations and examples b…
Large-sample properties of unsupervised estimation of the linear discriminant using projection pursuit
2021
We study the estimation of the linear discriminant with projection pursuit, a method that is unsupervised in the sense that it does not use the class labels in the estimation. Our viewpoint is asymptotic and, as our main contribution, we derive central limit theorems for estimators based on three different projection indices, skewness, kurtosis, and their convex combination. The results show that in each case the limiting covariance matrix is proportional to that of linear discriminant analysis (LDA), a supervised estimator of the discriminant. An extensive comparative study between the asymptotic variances reveals that projection pursuit gets arbitrarily close in efficiency to LDA when the…
Modelling and prediction of perceptual segmentation
2017
While listening to music, we somehow make sense of a multiplicity of auditory events; for example, in popular music we are often able to recognize whether the current section is a verse or a chorus, and to identify the boundaries between these segments. This organization occurs at multiple levels, since we can discern motifs, phrases, sections and other groupings. In this work, we understand segment boundaries as instants of significant change. Several studies on music perception and cognition have strived to understand what types of changes are associated with perceptual structure. However, effects of musical training, possible differences between real-time and non real-time segmentation, and…
A Bayesian Reconstruction of a Historical Population in Finland, 1647–1850
2020
This article provides a novel method for estimating historical population development. We review the previous literature on historical population time-series estimates and propose a general outline to address the well-known methodological problems. We use a Bayesian hierarchical time-series model that allows us to integrate the parish-level data set and prior population information in a coherent manner. The procedure provides us with model-based posterior intervals for the final population estimates. We demonstrate its applicability by estimating the long-term development of Finlands population from 1647 onward and simultaneously place the country among the very few to have an annual popula…
Aluekiintiöinti ositetussa otannassa
2014
Aluetilastoja tuotetaan paljon kyselytutkimuksilla, joissa tietojen keruu nojautuu erilaisiin otanta-asetelmiin. Näissä asetelmissa alue määrittyy yleensä ositteeksi ja aluetunnusluvut ositetason estimaateiksi. Keskeinen kysymys tällaisessa tilanteessa on, miten otos kiintiöityy alueiden kes-ken. Taustalla on siis kiintiöintiongelma, jonka seurauksena joillekin alueille tulee vähän tai ei lainkaan otoshavaintoja. Tästä syystä alue-estimoinnissa on yleistä käyttää suorien otanta-asetelmaperusteisten estimaattien sijasta malliavusteisia tai –perusteisia estimaatteja, jotta kaikil-le alueille saataisiin riittävän tarkat estimaatit halutuista tunnusluvuista. Aluekiintiöinti sinällään on monitah…
Uncertainties in the heat conduction problems and reliable estimates
2013
The heat conduction problems for anisotropic bodies are studied taking into account the uncertainties in the material orientation. The best estimations of the upper and lower bounds of the considered energy dissipation functional are based on the developing new approach consisting in solution of some optimization problems and finding the extremal internal material structures, which realize minimal and maximal dissipation.
Optimal sample allocation conditioned on a small area model, estimator, and auxiliary data
2018
We have studied optimal sample allocation, associated with small area estimation, when the objective is to obtain as accurate estimates as possible, for the population and for the subpopulations, called as areas here. It is a question of a two-level optimization problem. The basic premise is composed of planned areas, stratified sampling, and small overall sample size predetermined by restricted time and budget resources. Low sample sizes are common in market surveys. During this thesis, we have developed new allocation methods, based on a small area model, estimator, and auxiliary data. The final method, the three-term Pareto allocation, is based on the three terms of the mean-squared erro…
Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network
2022
Various biotic and abiotic stresses are causing decline in forest health globally. Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly causing widespread tree mortality in northern latitudes as a consequence of the warming climate. Remote sensing using unoccupied aerial systems (UAS) together with evolving machine learning techniques provide a powerful tool for fast-response monitoring of forest health. The aim of this study was to investigate the performance of a deep one-stage object detection neural network in the detection of damage by I. typographus in Norway spruce trees using UAS RGB images. A Scaled…
Regressiomenetelmiä viljapellon biomassan estimointiin ortokuvista ja digitaalisesta korkeusmallista
2012
Tutkielmassa esitellään käyttötarkoitus biomassan estimoinnille ja vertaillaan kolmea regressiomenetelmää, lineaarista regressiota, k:n lähimmän naapurin menetelmää sekä tukivektoriregressiota. Tutkielmassa esitellään myös aineisto ja aineistoon suoritetut muunnokset.