Can rising housing prices explain China’s high household saving rate?

China’s average household saving rate is one of the highest in the world. One popular view attributes the high saving rate to fast rising housing prices and other costs of living in China. This article uses simple economic logic to show that rising housing prices and living costs per se cannot explain China’s high household saving rate. Although borrowing constraints and demographic changes can help translate housing prices to the aggregate saving rate, quantitative simulations using Chinese data on household income, housing prices, and demographics indicate that rising mortgage costs contribute at most 5 percentage points to the Chinese aggregate household saving rate, given the down-payment structure of China’s mortgage markets.Economic conditions - China ; Housing - Prices ; Consumer behavior ; China

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and is available on CRAN.Comment: The original title dated back to May 2015 is "Bootstrap Tests on High Dimensional Covariance Matrices with Applications to Understanding Gene Clustering

Multiple discrete soluble aggregates influence polyglutamine toxicity in a Huntington\u27s disease model system

Huntington’s disease (HD) results from expansions of polyglutamine stretches (polyQ) in the huntingtin protein (Htt) that promote protein aggregation, neurodegeneration, and death. Since the diversity and sizes of the soluble Htt-polyQ aggregates that have been linked to cytotoxicity are unknown, we investigated soluble Htt-polyQ aggregates using analytical ultracentrifugation. Soon after induction in a yeast HD model system, non-toxic Htt-25Q and cytotoxic Htt-103Q both formed soluble aggregates 29S to 200S in size. Because current models indicate that Htt-25Q does not form soluble aggregates, reevaluation of previous studies may be necessary. Only Htt-103Q aggregation behavior changed, however, with time. At 6 hr mid-sized aggregates (33S to 84S) and large aggregates (greater than 100S) became present while at 24 hr primarily only mid-sized aggregates (20S to 80S) existed. Multiple factors that decreased cytotoxicity of Htt-103Q (changing the length of or sequences adjacent to the polyQ, altering ploidy or chaperone dosage, or deleting anti-aging factors) altered the Htt-103Q aggregation pattern in which the suite of mid-sized aggregates at 6 hr were most correlative with cytotoxicity. Hence, the amelioration of HD and other neurodegenerative diseases may require increased attention to and discrimination of the dynamic alterations in soluble aggregation processes

Bose-Einstein condensation in an optical lattice

In this paper we develop an analytic expression for the critical temperature for a gas of ideal bosons in a combined harmonic lattice potential, relevant to current experiments using optical lattices. We give corrections to the critical temperature arising from effective mass modifications of the low energy spectrum, finite size effects and excited band states. We compute the critical temperature using numerical methods and compare to our analytic result. We study condensation in an optical lattice over a wide parameter regime and demonstrate that the critical temperature can be increased or reduced relative to the purely harmonic case by adjusting the harmonic trap frequency. We show that a simple numerical procedure based on a piecewise analytic density of states provides an accurate prediction for the critical temperature.Comment: 10 pages, 5 figure

Background Subtraction via Generalized Fused Lasso Foreground Modeling

Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts

Stable Feature Selection from Brain sMRI

Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. the lasso, are applied to select the optimal features and achieve high diagnosis accuracy. The lasso, however, usually results in independent unstable features. Stability, a manifest of reproducibility of statistical results subject to reasonable perturbations to data and the model, is an important focus in statistics, especially in the analysis of high dimensional data. In this paper, we explore a nonnegative generalized fused lasso model for stable feature selection in the diagnosis of Alzheimer's disease. In addition to sparsity, our model incorporates two important pathological priors: the spatial cohesion of lesion voxels and the positive correlation between the features and the disease labels. To optimize the model, we propose an efficient algorithm by proving a novel link between total variation and fast network flow algorithms via conic duality. Experiments show that the proposed nonnegative model performs much better in exploring the intrinsic structure of data via selecting stable features compared with other state-of-the-arts