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Estimation of fixed effects panel regression models with separable and nonseparable space-time filters
Spatial autoregression Panel data Spatial cointegration Explosive roots Fixed e¤ects
2016/1/20
This paper considers a quasi-maximum likelihood estimation for a linear panel data model with time and individual …xed e¤ects, where the disturbances have dynamic and spatial correlations which might ...
Transcription factor-pathway co-expression analysis reveals cooperation between SP1 and ESR1 on dysregulating cell cycle arrest in non-hyperdiploid multiple myeloma
coexpression cell cycle arrest multiple myeloma hyperdiploid
2016/1/20
Multiple myeloma is a hematological cancer of plasma B cells and remains incurable. Two major subtypes of myeloma, hyperdiploid MM (HMM) and non-hyperdiploid MM (NHMM), have distinct chromosomal alter...
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
α-mixing, dimension reduction instrument variables nonstationarity time series
2016/1/20
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors,a linear combination of some la...
High Dimensional Influence Measure
Cook’s distance High dimensional diagnosis Influential obser- vation Lasso Marginal correlations Variable Screening
2016/1/20
Influence diagnosis is important since presence of influential ob-servations could lead to distorted analysis and misleading interpreta-tions. For high dimensional data, it is particularly so, as the ...
Construction of sliced space-ˉlling designs based on balanced sliced orthogonal arrays
Balanced computer experiment difference matrix Latin hypercube design
2016/1/20
Latin hypercube designs have been widely used in computer experiments with quantitative factors. When there are both qualitative and quantitative fac-tors in computer experiments, sliced space-filling...
Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing Paid Search Advertising Partial Covariance
2016/1/20
In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically,we employ the partial covariances be...
Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
2016/1/20
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
Testing Additive Separability of Error Term in Nonparametric Structural Models
Additive Separability Hypotheses Testing Nonparametric Structural Equation
2016/1/20
This paper considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model non-additively.We...
Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting
Nonlinearity Nonparametric regression Semiparametric regression Local mono- tonicity Bagging
2016/1/20
This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang(2001). Asymptotic properties of ...
On Implied Volatility for Options – Some Reasons to Smile and More to Correct
Bias correction Implied volatility Kernel estimator Pricing errors
2016/1/20
We analyze the properties of the implied volatility, the commonly used volatility estimator by direct option price inversion. It is found that the implied volatility is subject to a systematic bias in...
Testing the statistical significance of an ultra-high-dimensional naïve Bayes classfier
Binary Predictor Hypothesis Testing Na?ve Bayes Supervised Learning
2016/1/20
The na?ve Bayes approach is one of the most popular methods used for classi?cation. Nevertheless, how to test its statistical signi?cance under an ultra-high-dimensional(UHD) setup is not well underst...
Weak extinction versus global exponential growth of total mass for superdi usions corresponding to the operator Lu + βu − ku2
superdiffusion superprocess measure-valued process gauge theorem Kato class growth bound principal eigenvalue
2016/1/20
Consider a superdiffusion X on R d corresponding to the semilinear operator A(u) = Lu + βu − ku 2 , where L is a second order elliptic operator, β(·) is in the Kato class and bounded from above,...
Small Value Probabilities for Supercritical Branching Processes with Immigration
Supercritical Galton-Watson branching process small value probability immigration
2016/1/20
We consider a supercritical Galton-Watson branching process with immigration. It is well known that under suitable conditions on the offspring and immigration distributions, there is a finite,strictly...
On Pattern Recovery of The Fused Lasso
Fused Lasso Non-asymptotic Pattern recovery Preconditioning
2016/1/20
We study the property of the Fused Lasso Signal Approximator(FLSA) for estimating a blocky signal sequence with additive noise.We transform the FLSA to an ordinary Lasso problem. By studying the prope...
Bessel Processes, Stochastic Volatility, and Timer Options
Bessel Processes Stochastic Volatility Timer Options
2016/1/20
Motivated by analytical valuation of timer options (an important innovation in realized variance based derivatives), we explore their novel mathematical connection with stochastic volatility and Besse...