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Biodiversity conservation in forest fragments surrounded by a low-quality matrix requires an understanding of how ecological conditions prevailing in the matrix enter the fragments and interact with local habitat conditions. We assessed the regeneration of oak species along edge–interior gradients in forest fragments at the periphery of Mexico City. The abundance of oak saplings was sampled along transects to the forest, while the edge effect was analysed using segmented zero-inflated Poisson models for abundance data. Three oak species were dominant in terms of their relative abundances: Quercus laeta, Quercus castanea and Quercus obtusata. Regeneration of nine oak species responded nonlinearly to the edge distance, with greater sapling abundance from the edge up to 10 m into the fragment. Canopy cover and tree height decreased from edge to fragment interior, while saplings increased in open areas within the fragments (i.e., independent of edge distance). A posterior analysis indicated that Q. obtusata reacted positively to edges. These results indicate that oak regeneration is promoted by suitable habitat conditions near the boundaries. Therefore, we suggest that forest management should focus on promoting seed production and oak establishment in forest interior habitats.
Disagreement is a ubiquitous feature of human life, and philosophers have dutifully attended to it. One important question related to disagreement is epistemological: How does a rational person change her beliefs (if at all) in light of disagreement from others? The typical methodology for answering this question is to endorse a steadfast or conciliatory disagreement norm (and not both) on a priori grounds and selected intuitive cases. In this paper, I argue that this methodology is misguided. Instead, a thoroughgoingly Bayesian strategy is what's needed. Such a strategy provides conciliatory norms in appropriate cases and steadfast norms in appropriate cases. I argue, further, that the few extant efforts to address disagreement in the Bayesian spirit are laudable but uncompelling. A modelling, rather than a functional, approach gets us the right norms and is highly general, allowing the epistemologist to deal with (1) multiple epistemic interlocutors, (2) epistemic superiors and inferiors (i.e. not just epistemic peers), and (3) dependence between interlocutors.
This article proposes a unified framework for solving and estimating linear rational expectations models with a variety of frequency-domain techniques, some established, some new. The solution methodology is applicable to a wide class of models and leads to straightforward construction of the spectral density for performing likelihood-based inference. We also generalize the well-known spectral decomposition of the Gaussian likelihood function to a composite version implied by several competing models. Taken together, these techniques yield fresh insights into the model’s theoretical and empirical implications beyond conventional time-domain approaches can offer. We illustrate the proposed framework using a prototypical new Keynesian model with fiscal details and two determinate monetary–fiscal policy regimes. The model is simple enough to deliver an analytical solution that makes the policy effects transparent under each regime, yet still able to shed light on the empirical interactions between US monetary and fiscal policies along different frequencies.
In political science, data with heterogeneous units are used in many studies, such as those involving legislative proposals in different policy areas, electoral choices by different types of voters, and government formation in varying party systems. To disentangle decision-making mechanisms by units, traditional discrete choice models focus exclusively on the conditional mean and ignore the heterogeneous effects within a population. This paper proposes a conditional binary quantile model that goes beyond this limitation to analyze discrete response data with varying alternative-specific features. This model offers an in-depth understanding of the relationship between the explanatory and response variables. Compared to conditional mean-based models, the conditional binary quantile model relies on weak distributional assumptions and is more robust to distributional misspecification. The model also relaxes the assumption of the independence of irrelevant alternatives, which is often violated in practice. The method is applied to a range of political studies to show the heterogeneous effects of explanatory variables across the conditional distribution. Substantive interpretations from counterfactual scenarios are used to illustrate how the conditional binary quantile model captures unobserved heterogeneity, which extant models fail to do. The results point to the risk of averaging out the heterogeneous effects across units by conditional mean-based models.
To control hepatitis A spread by vaccination, accurate estimation of transmissibility is vital. Regan et al. (2016) proposed a model of hepatitis A virus (HAV) transmission and used least squares to calibrate model to the 1991/1992 HAV outbreak in men who have sex with men (MSM) in Sydney, Australia. Based on the estimate of R0, they obtained the critical immunity of 70% and showed that when the proportion immune <70%, there is a definite chance for outbreaks to take place. The immunity level from previous surveys ranges from 32% to 64% after 1996 while no outbreaks in Australian MSMs have been reported since 1996. Further noticing the ill-distributed parameters, we argue that their estimate of R0 is not accurate. In this study, we revisited their model by Bayesian inference, which has privilege over least squares. We obtained the appropriate posterior distributions of parameters and the estimate of R0 ranges from 1.38 to 2.89, indicating a critical immunity of 65%. The reduction in critical immunity and outbreak probabilities predicts the absence of outbreaks in Australian MSMs since 1996. Our study shows the importance of using appropriate methods to provide reliable and accurate estimates of the model parameters especially the transmissibility.
In this article, we study parameter uncertainty and its actuarial implications in the context of economic scenario generators. To account for this additional source of uncertainty in a consistent manner, we cast Wilkie’s four-factor framework into a Bayesian model. The posterior distribution of the model parameters is estimated using Markov chain Monte Carlo methods and is used to perform Bayesian predictions on the future values of the inflation rate, the dividend yield, the dividend index return and the long-term interest rate. According to the US data, parameter uncertainty has a significant impact on the dispersion of the four economic variables of Wilkie’s framework. The impact of such parameter uncertainty is then assessed for a portfolio of annuities: the right tail of the loss distribution is significantly heavier when parameters are assumed random and when this uncertainty is estimated in a consistent manner. The risk measures on the loss variable computed with parameter uncertainty are at least 12% larger than their deterministic counterparts.
In the article  a hierarchy of modal logics has been defined to capture the logical features of Bayesian belief revision. Elements in that hierarchy were distinguished by the cardinality of the set of elementary propositions. By linking the modal logics in the hierarchy to the modal logics of Medvedev frames it has been shown that the modal logic of Bayesian belief revision determined by probabilities on a finite set of elementary propositions is not finitely axiomatizable. However, the infinite case remained open. In this article we prove that the modal logic of Bayesian belief revision determined by standard Borel spaces (these cover probability spaces that occur in most of the applications) is also not finitely axiomatizable.
Two new species of the genus Aporcelinus from the USA are described and illustrated. Aporcelinus floridensis sp. n. is characterized by its 1.12–1.52 mm long body, lip region offset by marked constriction and 14.5–17.0 μm broad with perioral liplets, odontostyle 16.5–20.0 μm at its ventral side and 1.1–1.2 times the lip region diameter, neck 316–395 μm long, pharyngeal expansion occupying 43–48% of total neck length, uterus simple and 33–56 μm long or 0.8–1.2 times the corresponding body diameter, V = 48–54, female tail conical (36–49 μm long, c = 27–41, c’ = 1.2–2.0) with finely rounded terminus and no hyaline portion, and male absent. Aporcelinus paolae sp. n. is characterized by its 1.29–1.80 mm long body, lip region offset by marked constriction and 14–16 μm broad, odontostyle 15–17 μm at its ventral side and 1.0–1.1 times the lip region diameter, neck 314–397 μm long, pharyngeal expansion occupying 43–53% of total neck length, uterus tripartite and 128–164 μm long or 2.6–3.6 times the corresponding body diameter, V = 53–57, female tail conical (30–39 μm long, c = 40–51, c’ = 1.1–1.3) with finely rounded terminus and variably re-curved dorsad, male tail conical (27–36 μm, c = 39–59, c’ = 0.9–1.2), ventrally straight and dorsally convex, spicules 48–54 μm long, and 7–9 irregularly spaced ventromedian supplements lacking hiatus. The analyses of the D2-D3 expansion segments of 28S rRNA (LSU) gene sequences of the two new species confirmed the monophyly of the genus, based upon currently available data, showing a close relationship between the genera Aporcelinus and Makatinus, and justified the placement of Aporcelaimellus, Makatinus and Aporcelinus under the subfamily Aporcelaimellinae.
This paper describes automatic music transcription with chord estimation for music audio signals. We focus on the fact that concurrent structures of musical notes such as chords form the basis of harmony and are considered for music composition. Since chords and musical notes are deeply linked with each other, we propose joint pitch and chord estimation based on a Bayesian hierarchical model that consists of an acoustic model representing the generative process of a spectrogram and a language model representing the generative process of a piano roll. The acoustic model is formulated as a variant of non-negative matrix factorization that has binary variables indicating a piano roll. The language model is formulated as a hidden Markov model that has chord labels as the latent variables and emits a piano roll. The sequential dependency of a piano roll can be represented in the language model. Both models are integrated through a piano roll in a hierarchical Bayesian manner. All the latent variables and parameters are estimated using Gibbs sampling. The experimental results showed the great potential of the proposed method for unified music transcription and grammar induction.
In this paper, we analyze the effects of the stimulus packages adopted by the German government during the Great Recession. We employ a standard medium-scale dynamic stochastic general equilibrium (DSGE) model extended by non-optimizing households and a detailed fiscal sector. In particular, the dynamics of spending and revenue variables are modeled as feedback rules with respect to the cyclical components of output, hours worked and private investment. Based on the estimated rules, fiscal shocks are identified. According to the results, fiscal policy, in particular public consumption, investment, and transfers prevented a sharper and prolonged decline of German output at the beginning of the Great Recession, suggesting a timely response of fiscal policy. The overall effects, however, are small when compared to other domestic and international shocks that contributed to the economic downturn. Our overall findings are not sensitive to considering fiscal foresight.
Latent stochastic blockmodels are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper, we propose a new Bayesian framework to characterize the construction of connections. We rely on a Markovian property to describe the evolution of nodes' cluster memberships over time. We recast the problem of clustering the nodes of the network into a model-based context, showing that the integrated completed likelihood can be evaluated analytically for a number of likelihood models. Then, we propose a scalable greedy algorithm to maximize this quantity, thereby estimating both the optimal partition and the ideal number of groups in a single inferential framework. Finally, we propose applications of our methodology to both real and artificial datasets.
Cohort effects are important factors in determining the evolution of human mortality for certain countries. Extensions of dynamic mortality models with cohort features have been proposed in the literature to account for these factors under the generalised linear modelling framework. In this paper we approach the problem of mortality modelling with cohort factors incorporated through a novel formulation under a state-space methodology. In the process we demonstrate that cohort factors can be formulated naturally under the state-space framework, despite the fact that cohort factors are indexed according to year-of-birth rather than year. Bayesian inference for cohort models in a state-space formulation is then developed based on an efficient Markov chain Monte Carlo sampler, allowing for the quantification of parameter uncertainty in cohort models and resulting mortality forecasts that are used for life expectancy and life table constructions. The effectiveness of our approach is examined through comprehensive empirical studies involving male and female populations from various countries. Our results show that cohort patterns are present for certain countries that we studied and the inclusion of cohort factors are crucial in capturing these phenomena, thus highlighting the benefits of introducing cohort models in the state-space framework. Forecasting of cohort models is also discussed in light of the projection of cohort factors.
This paper adds to the large body of literature on the effects of technology shocks empirically and theoretically. Using a structural vector error correction model, we first provide evidence that not only hours but also investment decline temporarily following a technology improvement. This result is robust to important data and identification issues addressed in the literature. We then show that the negative response of inputs is consistent with an estimated monetary model in which the presence of strategic complementarity in price setting, in addition to nominal rigidities, lowers the sensitivity of prices to marginal costs, and monetary policy does not fully accommodate the shock.
This paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models. The framework we adopt is to reinterpret popular mortality models such as the Lee–Carter class of models in a general state-space modelling methodology, which allows modelling, estimation and forecasting of mortality under a unified framework. We propose alternative model identification constraints which are more suited to statistical inference in filtering and parameter estimation. We then develop a class of Bayesian state-space models which incorporate a priori beliefs about the mortality model characteristics as well as for more flexible and appropriate assumptions relating to heteroscedasticity that present in observed mortality data. To study long-term mortality dynamics, we introduce stochastic volatility to the period effect. The estimation of the resulting stochastic volatility model of mortality is performed using a recent class of Monte Carlo procedure known as the class of particle Markov chain Monte Carlo methods. We illustrate the framework using Danish male mortality data, and show that incorporating heteroscedasticity and stochastic volatility markedly improves model fit despite an increase of model complexity. Forecasting properties of the enhanced models are examined with long-term and short-term calibration periods on the reconstruction of life tables.
M-dwarf stars are more abundant than G-dwarf stars, so our position as observers on a planet orbiting a G-dwarf raises questions about the suitability of other stellar types for supporting life. If we consider ourselves as typical, in the anthropic sense that our environment is probably a typical one for conscious observers, then we are led to the conclusion that planets orbiting in the habitable zone of G-dwarf stars should be the best place for conscious life to develop. But such a conclusion neglects the possibility that K-dwarfs or M-dwarfs could provide more numerous sites for life to develop, both now and in the future. In this paper we analyse this problem through Bayesian inference to demonstrate that our occurrence around a G-dwarf might be a slight statistical anomaly, but only the sort of chance event that we expect to occur regularly. Even if M-dwarfs provide more numerous habitable planets today and in the future, we still expect mid G- to early K-dwarfs stars to be the most likely place for observers like ourselves. This suggests that observers with similar cognitive capabilities as us are most likely to be found at the present time and place, rather than in the future or around much smaller stars.
Différentes heuristiques ont été avancées par les psychologues et les économistes afin de rendre compte des comportements sur les marchés financiers. Elles soulignent les biais cognitifs qui affectent les croyances individuelles, et s'efforcent d'expliquer dans une certaine mesure les anomalies constatées sur les marchés financiers. L'expérimentation menée vise à tester les heuristiques de conservatisme, de représentativité et d'ancrage-ajustement dans un contexte dynamique de quinze périodes : les sujets reçoivent, à chaque période, une information financière et révisent individuellement leurs croyances quant à la qualité d'une entreprise. Les croyances observées s'avèrent incompatibles avec l'hypothèse de révision bayésienne: les sujets ont tendance à surévaluer les petites probabilités et à sous-évaluer les fortes probabilités. L'heuristique de représentativité est, de la même manière, invalidée : le traitement économétrique montre que les sujets sous-pondèrent les signaux les plus intenses, preuve qu'ils ne tirent pas parti de leurs intensités informationnelles. Les hypothèses de conservatisme et d'ancrage-ajustement sont au contraire conjointement validées : les sujets sous-pondèrent l'information nouvelle quand ils révisent leurs croyances mais ce comportement de révision est pleinement conditionné au fait que les sujets s'écartent ou se rapprochent d'une valeur d'ancrage.
The objective of this work was to estimate genetic parameters for a measure of persistency of milk yield and to evaluate its association with 305-d cumulative milk yield and lactation length. 12 346 records from 8202 dairy Gyr cows including lactations up to fifth calving were used. The measure of persistency was obtained from one of the parameters of a quadratic model that describes the cumulative yield across lactation as a function of days in milk. A three-trait multivariate analysis was done. Heritability and repeatability for persistency were 0·08 and 0·21, respectively. Deviance Information Criterion provided evidence that the additive genetic covariance between the measure of persistency studied and 305-d cumulative yield is zero. Genetic correlations between persistency and lactation length were 0·50 and 0·27 for first or all lactations, respectively. Milk yield persistency as measured in this study has low heritability. Selection for persistency can increase lactation length. The measure of milk yield persistency studied here is genetically independent of total milk yield and can be included in routine genetic evaluations of dairy cattle.
An essential quantity required to understand the physics of the early Universe is the primordial scalar potential and its statistics. We present an inexpensive all-sky reconstruction of the potential from CMB temperature data as well as an extension including polarization data. Once explicitly having the potential, its statistics and underlying physics can be directly obtained avoiding expensive CMB analyses.