7,406 research outputs found
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
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
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
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