I've been working with this Excel formula for a month or so. It comes from Leila Gharani's Youtube tutorial.

=IF(ROWS($A$1000:$A1000)<$J$291,INDEX($B$2:$B$300,AGGREGATE(15,3,($N$2:$N$300="Japanese")/($N$2:$N$300="Japanese")*ROW($N$2:$N$300)-ROW($N$1),ROWS($A$1000:$A1000)))," ")

In this iteration, it's indexing column B, which is a list of movie names, and returning a list of every Japanese language film. Film languages are listed in column N. The formula takes advantage of Aggregate's "Ignore error" option; since Excel treats yeses as 1's and nos as 0's, dividing the aggregate results by itself returns an error for all the nos, since you can't divide by zero. Pretty clever. Then the formula multiplies the 1 by the row where it's located, and finally returns the smallest number in the list to the index function (then the second smallest, then third smallest as you drag down the formula).

My question is, how do I add criteria so the film not only has to be in Japanese, but also has to have a RottenTomatoes score of >75%, if Column T is RottenTomatoes scores? I'm feel I should just multiply the Japanese criteria by the RT criteria in brackets and then divide that product by itself, but I keep getting errors when I try this. Maybe my syntax is screwy?

And yes, I know it would be a lot easier to do this using VBA, but I'm running the workbook on Sharepoint, which doesn't support VBA.

Thanks!

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Memoirs of the AMS 224 (2012).
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Chuanan Wei
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Christian Rau.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 549--557.

Abstract:
We revisit a shape inversion formula derived by Panaretos in the context of a particle density estimation problem with unknown rotation of the particle. A distribution is presented which imitates, or “fakes”, the uniformity or Haar distribution that is part of that formula.

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Xiaoqin Wang, Li Yin.

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Abstract:
In sequential causal inference, two types of causal effects are of practical interest, namely, the causal effect of the treatment regime (called the sequential causal effect) and the blip effect of treatment on the potential outcome after the last treatment. The well-known $G$-formula expresses these causal effects in terms of the standard parameters. In this article, we obtain a new $G$-formula that expresses these causal effects in terms of the point observable effects of treatments similar to treatment in the framework of single-point causal inference. Based on the new $G$-formula, we estimate these causal effects by maximum likelihood via point observable effects with methods extended from single-point causal inference. We are able to increase precision of the estimation without introducing biases by an unsaturated model imposing constraints on the point observable effects. We are also able to reduce the number of point observable effects in the estimation by treatment assignment conditions.

Gianluca Mastrantonio, Clara Grazian, Sara Mancinelli, Enrico Bibbona.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2483--2508.

Abstract:
Improved communication systems, shrinking battery sizes and the price drop of tracking devices have led to an increasing availability of trajectory tracking data. These data are often analyzed to understand animal behavior. In this work, we propose a new model for interpreting the animal movent as a mixture of characteristic patterns, that we interpret as different behaviors. The probability that the animal is behaving according to a specific pattern, at each time instant, is nonparametrically estimated using the Logistic-Gaussian process. Owing to a new formalization and the way we specify the coregionalization matrix of the associated multivariate Gaussian process, our model is invariant with respect to the choice of the reference element and of the ordering of the probability vector components. We fit the model under a Bayesian framework, and show that the Markov chain Monte Carlo algorithm we propose is straightforward to implement. We perform a simulation study with the aim of showing the ability of the estimation procedure to retrieve the model parameters. We also test the performance of the information criterion we used to select the number of behaviors. The model is then applied to a real dataset where a wolf has been observed before and after procreation. The results are easy to interpret, and clear differences emerge in the two phases.

Nicolas Garcia Trillos, Daniel Sanz-Alonso.

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Abstract:
The Bayesian update can be viewed as a variational problem by characterizing the posterior as the minimizer of a functional. The variational viewpoint is far from new and is at the heart of popular methods for posterior approximation. However, some of its consequences seem largely unexplored. We focus on the following one: defining the posterior as the minimizer of a functional gives a natural path towards the posterior by moving in the direction of steepest descent of the functional. This idea is made precise through the theory of gradient flows, allowing to bring new tools to the study of Bayesian models and algorithms. Since the posterior may be characterized as the minimizer of different functionals, several variational formulations may be considered. We study three of them and their three associated gradient flows. We show that, in all cases, the rate of convergence of the flows to the posterior can be bounded by the geodesic convexity of the functional to be minimized. Each gradient flow naturally suggests a nonlinear diffusion with the posterior as invariant distribution. These diffusions may be discretized to build proposals for Markov chain Monte Carlo (MCMC) algorithms. By construction, the diffusions are guaranteed to satisfy a certain optimality condition, and rates of convergence are given by the convexity of the functionals. We use this observation to propose a criterion for the choice of metric in Riemannian MCMC methods.

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Although soy protein–based infant formula is known to support physical growth equal to that of infants fed cow's milk–based formula, data are lacking on developmental status of infants fed soy formula compared with breast milk or milk formula.

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