Australian Residential Housing Market & Hedonic Construction of House Price Indices for Metropolitan

A Semiparametric spatial model is used as it allows nonlinear estimation of both mean and variance. A Bayesian approach is used for inference via a Markov Chain Monte Carlo sampling scheme. A distinct advantage of using the Bayesian approach is the incorporation of prior information in the inferential process. The prior is updated with arrival of information. In the real world, the modeller should have some idea of the outcome before the modelling process begins. Finite sample inference can be obtained and is more accurate than asymptotic approximation. In the case of the real estate market, transaction data are finite due to infrequent trading. Estimation is done via posterior distributions which factor in the variability of estimators and therefore have improved confidence intervals. Spatial variables such as longitude and latitude are modelled via the construction of a bivariate thin plate spline. These two variables provide powerful lens for capturing the effect of demographic factors and for borrowing and lending information in neighbouring suburbs. Demographic factors and 1 trends are just as important as economic factors in determining demand for residential housing and they are also included in the model

Australian Residential Housing Market & Hedonic Construction of House Price Indices for Metropolitan

A Semiparametric spatial model is used as it allows nonlinear estimation of both mean and variance. A Bayesian approach is used for inference via a Markov Chain Monte Carlo sampling scheme. A distinct advantage of using the Bayesian approach is the incorporation of prior information in the inferential process. The prior is updated with arrival of information. In the real world, the modeller should have some idea of the outcome before the modelling process begins. Finite sample inference can be obtained and is more accurate than asymptotic approximation. In the case of the real estate market, transaction data are finite due to infrequent trading. Estimation is done via posterior distributions which factor in the variability of estimators and therefore have improved confidence intervals. Spatial variables such as longitude and latitude are modelled via the construction of a bivariate thin plate spline. These two variables provide powerful lens for capturing the effect of demographic factors and for borrowing and lending information in neighbouring suburbs. Demographic factors and 1 trends are just as important as economic factors in determining demand for residential housing and they are also included in the model

Locally Adaptive Nonparametric Binary Regression

A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of probit regressions with the argument of each probit regression having a thin plate spline prior with its own smoothing parameter and with the mixture weights depending on the covariates. The estimator is compared to a single spline estimator and to a recently proposed locally adaptive estimator. The methodology is illustrated by applying it to both simulated and real examples.Comment: 31 pages, 10 figure

Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models

We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they enter linearly or flexibly. When the variance term is null we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation and the methodology is illustrated using real and simulated data sets.Comment: 8 graphs 35 page

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Bayesian modelling and forecasting of intra-day electricity load

With the advent of wholesale electricity markets there has been renewed focus on intra-day electricity load forecasting. This paper employs a multi-equation regression model with a diagonal first order stationary vector autoregresson (VAR) for modeling and forecasting intra-day electricity load. The correlation structure of the disturbances to the VAR and the appropriate subset of regressors are explored using Bayesian model selection methodology. The full spectrum of finite sample inference is obtained using a Bayesian Markov chain Monte Carlo sampling scheme. This includes the predictive distribution of load and the distribution of the time and level of daily peak load, something that is difficult to obtain with other methods of inference. The method is applied to several multi-equation models of half-hourly total system load in New South Wales, Australia. A detailed model based on three years of data reveals trend, seasonal, bivariate temperature/humidity and serial correlation components that all vary intra-day, justifying the assumption of a multi-equation approach. Short-term forecasts from simple models highlight the gains that can be made if accurate temperature predictions are exploited. Bayesian predictive means for half-hourly load compare favourably with point forecasts obtained using iterated generalized least squares estimation of the same models