The decomposition of housing market variations : a panel data approach

Purpose – This paper develops a new decomposition method of the housing market variations to analyse the housing dynamics of the Australian eight capital cities.
Design/methodology/approach – This study reviews the prior research on analysing the housing market variations and classifies the previous methods into four main models. Based on this, the study develops a new decomposition of the variations, which is made up of regional information, homemarket information and time information. The panel data regression method, unit root test and F test are adopted to construct the model and interpret the housing market variations of the Australian capital cities.
Findings – This paper suggests that the Australian home-market information has the same elasticity to the housing market variations across cities and time. In contrast, the elasticities of the regional information are distinguished. However, similarities exit in the west and north of Australia or the south and east of Australia. The time information contributes differently along the observing period, although the similarities are found in certain periods.
Originality/value – This paper introduces the housing market variation decomposition into the research of housing market variations and develops a model based on the new method of the housing market variation decomposition.

S-Kcore : a social-aware Kcore decomposition algorithm in pocket switched networks

The key nodes in network play the critical role in system recovery and survival. Many traditional key nodes selection algorithms utilize the characters of the physical topology to find the key nodes. But they can hardly succeed in the mobile ad hoc network due to the mobility nature of the network. In this paper we propose a social-aware Kcore selection algorithm to work in the Pocket Switched Network. The social view of the network suggests the social position of the mobile nodes can help to find the key nodes in the Pocket Switched Network. The S-Kcore selection algorithm is designed to exploit the nodes' social features to improve the performance in data communication. Experiments use the NS2 shows S-Kcore selection algorithm workable in the Pocket Switched Network. Furthermore, with the social behavior information, those key nodes are more suitable to represent and improve the whole network's performance.

Numerical implementation of a functional observability algorithm : a singular value decomposition approach

The paper outlines a numerical algorithm to implement the concept of Functional Observability introduced in [6] based on a Singular Value Decomposition approach. The key feature of this algorithm is in outputting a minimum number of additional linear functions of the state vector when the system is Functional Observable, these additional functions are required to design the smallest possible order functional observer as stated in [6].

Volatility decomposition of Australian housing prices

This study examines the volatility pattern of Australian housing prices. The approach for this research was to decompose the conditional volatility of housing prices into a “permanent” component and a “transitory” component via a Component-Generalized Autoregressive Conditional Heteroskedasticity (C-GARCH) model. The results demonstrate that the shock impact on the short-run component (transitory) is much larger than the long-run component (permanent), whereas the persistence of transitory shocks is much less than permanent shocks. Moreover, both permanent and transitory volatility components have different determinants. The results provide important new insights into the volatility pattern of housing prices which has direct implications for investment in housing by owner-occupiers and investors.

Quantifying heteroskedasticity via binary decomposition

This paper presents a quantifying measure for heteroskedasticity of a time series. In this research, heteroskedasticity levels are measured by decomposing the examined time series recursively into homoskedastic segments. Each segment of the examined time series is decomposed into smaller segments if it tests positively to heteroskedasticity tests. The final quantified value of the heteroskedasticity level is the number of homoskedastic segments. The proposed measure is robust and detects heteroskedasticity in small average variance datasets.

A novel bio-kinematic encoder for human exercise representation and decomposition - Part 1 : Indexing and modelling

Current bio-kinematic encoders use velocity, acceleration and angular information to encode human exercises. However, in exercise physiology there is a need to distinguish between the shape of the trajectory and its execution dynamics. In this paper we propose such a two-component model and explore how best to compute these components of an action. In particular, we show how a new spatial indexing scheme, derived directly from the underlying differential geometry of curves, provides robust estimates of the shape and dynamics compared to standard temporal indexing schemes.

A novel bio-kinematic encoder for human exercise representation and decomposition - Part 2 : Robustness and Optimisation

Bio-kinematic characterisations of human exercises constitute dealing with parameters such as velocity, acceleration, joint angles, etc. A majority of these are measured directly from various sensors ranging from RGB cameras to inertial sensors. However, due to certain limitations associated with these sensors, such as inherent noise, filters are required to be implemented to subjugate the effect from the noise. When the two-component (trajectory shape and dynamics) bio-kinematic encoding model is being established to represent an exercise, reducing the effect from noise embedded in raw data will be important since the underlying model can be quite sensitive to noise. In this paper, we examine and compare some commonly used filters, namely least-square Gaussian filter, Savitzky-Golay filter and optimal Kalman filter, with four groups of real data collected from Microsoft Kinectc , and assert that Savitzky- Golay filter is the best one when establishing an underlying model for human exercise representation.

A system decomposition approach to the design of functional observers

This paper reports a system decomposition that allows the construction of a minimum-order functional observer using a state observer design approach. The system decomposition translates the functional observer design problem to that of a state observer for a smaller decomposed subsystem. Functional observability indices are introduced, and a closed-form expression for the minimum order required for a functional observer is derived in terms of those functional observability indices. © 2014 Taylor & Francis.

A spatial decomposition approach for investigating house price convergences

 Convergence of house prices indicates how prices are reaching an aggregate equilibrium in a long-run perspective. Identifying the convergence is important for cross-region housing development and investment. Few studies have identified house price convergences at different levels, with spatial effects on house prices predominantly ignored. The research presented here developed a spatial panel regression approach to investigate the convergences of house prices in Australian capital cities. Three hypotheses were tested to identify the level of house price convergence. The results demonstrate that a steady state in a system of regional house prices and spatial effects contribute to the convergence continuing.

Mixing vector construction for single channel semi-blind source separation using empirical mode decomposition

The Empirical Mode Decomposition (EMD) method is a commonly used method for solving the problem of single channel blind source separation (SCBSS) in signal processing. However, the mixing vector of SCBSS, which is the base of the EMD method, has not yet been effectively constructed. The mixing vector reflects the weights of original signal sources that form the single channel blind signal source. In this paper, we propose a novel method to construct a mixing vector for a single channel blind signal source to approximate the actual mixing vector in terms of keeping the same ratios between signal weights. The constructed mixing vector can be used to improve signal separations. Our method incorporates the adaptive filter, least square method, EMD method and signal source samples to construct the mixing vector. Experimental tests using audio signal evaluations were conducted and the results indicated that our method can improve the similar values of sources energy ratio from 0.2644 to 0.8366. This kind of recognition is very important in weak signal detection.