Results 191 to 200 of about 858,976 (241)
Advanced Functional Materials, EarlyView.EuSc₂Te₄, an antiferromagnetic semiconductor, exhibits a nonlinear Hall effect (NLHE) characterized by quadratic current–voltage behavior. Combined experimental and theoretical studies reveal that this NLHE is linked to its antiferromagnetism and involves contributions from the quantum metric.
Seng Huat Lee +15 more
wiley +1 more source
Advanced Functional Materials, EarlyView.This study investigates the role of hole‐blocking layers (HBLs), particularly the combination of two HBLs in photomultiplication‐type organic photodetectors (PM‐OPDs), which results in high gain and record‐fast response. The proposed HBL strategy also allows for the use of photoactive layer with balanced donor–acceptor stoichiometry, which can be ...
Awais Sarwar +7 more
wiley +1 more source
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2019
This chapter recalls the basics of the estimation method consisting in maximizing the likelihood associated to the observations. The resulting estimators enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.
Julien Trufin +2 more
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This chapter recalls the basics of the estimation method consisting in maximizing the likelihood associated to the observations. The resulting estimators enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.
Julien Trufin +2 more
openaire +4 more sources
On the uniqueness of the maximum likelihood estimator
Economics Letters, 2002Abstract Orme [Econ. Rev. 8 (1989) 217] attempted to demonstrate that any solution to the likelihood equations for the truncated normal regression will be unique and will also be the global maximiser of the log-likelihood function. The argument rested on showing local concavity at any stationary point. Although sufficient in the scalar parameter case,
Orme, Chris D., Ruud, Paul A.
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2014
In Chap. 2 you learned that ordinary least squares (OLS) estimation minimizes the squared discrepancy between observed values and fitted ones. This procedure is primarily a descriptive tool, as it identifies the weights we use in our sample to best predict y from x.
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In Chap. 2 you learned that ordinary least squares (OLS) estimation minimizes the squared discrepancy between observed values and fitted ones. This procedure is primarily a descriptive tool, as it identifies the weights we use in our sample to best predict y from x.
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Maximum Kernel Likelihood Estimation
Journal of Computational and Graphical Statistics, 2008We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (MKLE). A speedy computational method to compute the MKLE based on binning is implemented in a simulation study which shows that ...
Jaki, Thomas, West, R. Webster
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1971
It is possible to develop a number of systems of estimation and nowhere does this seem to be more true than for the estimation of genetic crossover fractions. Several of these fail dismally because of inaccuracy and inefficiency (Fisher and Balmukand, 1928).
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It is possible to develop a number of systems of estimation and nowhere does this seem to be more true than for the estimation of genetic crossover fractions. Several of these fail dismally because of inaccuracy and inefficiency (Fisher and Balmukand, 1928).
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Moment Estimators and Maximum Likelihood
Biometrika, 1958where J'q2(x) P(x; 0) dx = Or, J'q(x) qq(x) P(x; 0) dx = 0 (r+ s). (2) To avoid undue complication at this stage we assume P(x; 0) is continuous throughout its range. We reconsider the restrictions on P in a subsequent section.
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2002
In this chapter maximum likelihood estimates (MLEs) of the parameters in growth curve models are discussed. Also expectations and variancecovariance matrices of the estimates are considered. In general, the MLE of the regression coefficient is different from the generalized least square estimate (GLSE) discussed in Chapter 2, because the former is a ...
Kai-Tai Fang, Jianxin Pan
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In this chapter maximum likelihood estimates (MLEs) of the parameters in growth curve models are discussed. Also expectations and variancecovariance matrices of the estimates are considered. In general, the MLE of the regression coefficient is different from the generalized least square estimate (GLSE) discussed in Chapter 2, because the former is a ...
Kai-Tai Fang, Jianxin Pan
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1982
This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
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This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
openaire +2 more sources